Reviewerly/exHarmony huggingface dataset:
Hubungan Perhatian Orang Tua Terhadap Disiplin Belajar Siswa Kelas V SD

The problem in this study is the lack of parental attention to students, so students are less disciplined in learning. This study is a quantitative study with a correlation design which aims to determine the relationship between parental attention and learning discipline for fifth grade students in SD Cluster II. The results showed that there was a significant relationship between parental attention to the learning discipline of fifth graders at SD Cluster II with a tcount value of (4.5302) greater (&gt;) ttable value (1.68488), which means H0 is rejected and H1 is accepted. Based on the analysis of the data above, it can be concluded that parental attention will have an impact on student learning discipline. So, the better the parents' attention, the better the student's learning discipline. From these conclusions, the impact resulting from this study is known that good parental attention will improve student learning discipline.
FedHF: A High Fairness Federated Learning Algorithm Based on Deconfliction in Heterogeneous Networks

In large-scale machine learning, federated learning (FL) is considered as a promising paradigm to address the problem of data privacy breach. Previous works have focused on improving fairness in terms of fair resource allocation. However, this is not sufficient considering that federated learning is essentially distributed training with average aggregation. Because low-contributing nodes, even if they are assigned more computational resources, are diluted by large-weighted nodes in aggregation. In particular, fair resource allocation for sophisticated systems is not realistic for real scenarios. In this paper, we propose FedHF, a new hierarchical fair federated learning framework with robust convergence and high fairness. FedHF improves upon naive combinations of federated learning and fair resource allocation with a hierarchy-based optimization of client selection algorithm and a conflict elimination method for fairness and discriminatory incentives. Through extensive experimental validation of our approach, we show that FedHF outperforms previous state-of-the-art methods.
A Pictureless World (From the Icon to “Saturated Phenomena” and Neural Networks)

The article considers how the world and culture might be understood without any recourse to the idea of a picture (“representation” would be the philosophical term). Three concrete examples illustrate the proposition that, within a culture that is oriented toward pictures (or representations), non-representable — immanent — forces are always at work. The Shroud of Turin clearly shows agraphic stains that resist signification. In the tradition of acheiropoietic images, they exist alongside symbolic elements of representation and suggest a new reading of the icon as a dynamic sign. Jean-Luc Marion attempts to create a version of phenomenology that would be adequate to the excess of the world. His term “saturated phenomenon” refers to disturbances that interrupt the intentional aim. Among them are paintings that indicate to what extent we are dazzled with pure visibility, and it is precisely pure visibility that saturates our sight. Finally, neural networks that have lately become ubiquitous provide clear evidence that rationality and causal relations are yielding to magic and correlations. Theories of memory that present brain function as a set of unlocalized and changing interactions are of great importance in this connection. In all three examples, the world surpasses human beings and challenges their very rationality. The world’s speeds can be captured through dynamic signs that are transformed together with the transformations of matter. Thus, the current cultural and technological situation reveals what has always been latently felt: a world neither encompassed by culture nor reducible to it. This world is much richer and more diverse than human ideas or representations. It is devoid of transcendence and resists objectification.
Dice Similarity and TF-IDF for New Student Admissions Chatbot

&#x0D; &#x0D; &#x0D; CS is one of the most important functions of any client-related organization, whether a business or a school (customer service). Notably from the committee responsible for student selection, CS, on the other hand, has a very limited capacity to be handled by humans, which can reduce university satisfaction. Therefore, we require technological assistance, which in this case takes the form of an AI-based chatbot. The objective of this study is to design and develop a chatbot system utilizing NLP (natural language processing) to aid the CS of the new student admissions committee at Pahlawan Tuanku Tambusai University in answering questions from prospective new students. The employed method is dice similarity weighted by TFIDF. The results of the conducted tests indicated that the recall rate was 100 percent and the precision reached 76.92 percent. The evaluation results indicate that the chatbot can effectively respond to questions from prospective students. &#x0D; &#x0D; &#x0D;
GAIM: Graph-aware Feature Interactional Model for Spam Movie Review Detection

Nowadays, more and more people decide whether to watch a certain movie by reading online movie reviews. Driven by large commercial interest, a growing number of spammers try to manipulate the online word-of-mouth of movies by publishing spam reviews. This has severely destroyed the credibility of movie review platforms and affected the healthy development of the lm industry. However, there is little research on the detection of spam movie reviews. Meanwhile, there are still great challenges for spam movie review detection, such as the lack of publicly available datasets, insufficient features, and low-performance detection models. In this paper, we firstly construct a dataset for spam movie reviews with our proposed labeling strategy and release it publicly. Secondly, we design 28 features to detect spam movie reviews, 13 of which are completely new features. Thirdly, in order to mine the characteristics of collusive attack behavior deeply, we propose a Graph-aware Feature Interactional Model (GAIM), which combines TextCNN, MLP (Multilayer Perceptron), and GAT (Graph Attention Network). After performance evaluation, the experimental results show that GAIM is more effective than the state-of-the-art baselines with an F1-score of 91.88% for spam movie review.
Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

Currently, with the dramatic increase in social media users and the greater variety of online product information, manual processing of this information is time-consuming and labour-intensive. Therefore, based on the text mining of online information, this paper analyzes the text representation method of online information, discusses the long short-term memory network, and constructs an interactive attention graph convolutional network (IAGCN) model based on graph convolutional neural network (GCNN) and attention mechanism to study the multimodal sentiment analysis (MSA) of online product information. The results show that the IAGCN model improves the accuracy by 4.78% and the F1 value by 29.25% compared with the pure interactive attention network. Meanwhile, it is found that the performance of the model is optimal when the GCNN is two layers and uses syntactic position attention. This research has important practical significance for MSA of online product information in social media.
Leveraging Bias in Pre-Trained Word Embeddings for Unsupervised Microaggression Detection

The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships.In this paper we present several extensions that improve both the quality of the vectors and the training speed.By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations.We also describe a simple alternative to the hierarchical softmax called negative sampling.An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases.For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada".Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.
The Effects of Discontinuing Machine Learning Decision Support

Advances in Machine Learning (ML) led organizations to increasingly implement predictive decision aids intended to improve employees’ decision-making performance. While such systems improve organizational efficiency in many contexts, they might be a double-edged sword when there is the danger of a system discontinuance. Following cognitive theories, the provision of ML-based predictions can adversely affect the development of decision-making skills that come to light when people lose access to the system. The purpose of this study is to put this assertion to the test. Using a novel experiment specifically tailored to deal with organizational obstacles and endogeneity concerns, we show that the initial provision of ML decision aids can latently prevent the development of decision-making skills which later becomes apparent when the system gets discontinued. We also find that the degree to which individuals 'blindly' trust observed predictions determines the ultimate performance drop in the post-discontinuance phase. Our results suggest that making it clear to people that ML decision aids are imperfect can have its benefits especially if there is a reasonable danger of (temporary) system discontinuances.
Fraud Detection in E-Commerce Using Machine Learning

A rise in transactions is being caused by an increase in online customers.We observe that the prevalence of misrepresentation in online transactions is also increasing. Device learning will become more widely used to avoid misrepresentation in online commerce. The goal of this investigation is to identify the best device learning calculation using decision trees, naive Bayes, random forests, and neural networks. The realities to be utilized have not yet been modified. Engineered minority over-testing stability information is made utilizing the strategy framework. The precision of the brain not entirely settled by
Text Detection Algorithm for Natural Scenes under Attention Supervision Strategy

The existing scene text detection algorithms will include redundant background area when dealing with arbitrary shape text regions. In order to automatically learn to focus on arbitrary shape text regions, a text detection algorithm is proposed under attention supervision strategy. Firstly, deep residual network is used as the skeleton network to extract feature maps containing multi-scale information, and then the fused feature maps are converted to generate attention masks through the attention mask generation module, and through the background suppression module, the next level feature map is generated by attention mask. Finally, the segmentation masks are generated through a series of convolution operations, and the final text detection results are obtained after post-processing optimization. Experimental results show that the proposed algorithm has superior multi-index comprehensive performance on the ICDAR2015 dataset, and the <italic>F</italic>-measure is 2.1% ahead of the comparison algorithm.
Membangun Budaya Literasi Masyarakat Pekon Sukananti Kecamatan Way Tenong Kabupaten Lampung Barat melalui Perpustakaan Desa dan Taman Baca Masyarakat

This service aims to describe the causes of the low interest in reading in the community, especially the young generation of school age in the village of Sukananti; diagnosing various causes of low interest in reading in the Sukananti village community; and designing a program of reading activities as an effort that can encourage people to want to read so that a literacy culture can be realized, especially for the young generation of school age in Sukananti village, Way Tenong district, West Lampung Regency through the provision of village libraries and community reading parks (TBM). The approach used in the implementation of this program and research is a qualitative approach in the form of a case study by examining events or events related to reading interest and literacy culture. Data collection techniques through observation, interviews, and data recording. Data analysis techniques are carried out through the presentation, analysis, description, and presentation of data related to the reading interest and culture of the community. In this case, to be able to build and increase interest in reading in order to foster a literacy culture, it is necessary to carry out various strategies and programs that can reach all levels of society, such as the provision of IT-based village library facilities, procurement of community reading parks, and can be pursued through the involvement of KKN students to teach. read and write for two to three months, and it is necessary to cooperate with the nearest school to provide access for the dropout community to get the opportunity to learn.
Whitening of greenhouse’s roof using drones and Petri net models

The Unmanned Aerial Vehicles (UAVs), commonly known as drones, are significant in the agriculture sphere to automate the work such as: data acquisition, crop spraying among others. This paper proposes a path planning solution for a team of UAVs that is required to whiten a greenhouse’s roof, this problem having both social-economic impact, as well as scientific-technical impact. First, the space around the roof is partitioned into cells based on a 3D cell decomposition technique, labeling the cells including the surface of the roof as regions of interest (ROIs). Based on this representation, a Petri net (PN) model captures the motion of drones, while their trajectories are returned by a Mixed Integer Linear Programming (MILP) problem which optimizes the energy consumption. An adaptable strategy is used such that the MILP problem considers only the available UAVs with enough energy to reach the ROIs. The algorithm is implemented in MATLAB and the simulation results are captured in a video link, evaluating the impact of the size of the team and the precision used for mapping the environment over the running time.
Classification of Complaint Categories in E-Commerce: A Case Study of PT Bukalapak

Bukalapak is one of the companies in Indonesia that is engaged in E-Commerce. It was recorded that in 2019, Bukalapak experienced a 230% increase in user growth compared to the previous year. Unfortunately, the growth in users is also followed by an increase in the number of complaints that occur in Bukalapak. It was recorded that in 2020, Bukalapak complaints increased by 50% at the end of 2020 when compared to the beginning of 2020. The increase in complaints led to an increase in the average handle time for complaints which led to a decrease in user satisfaction. 3 main issues that cause an increase in average handle time, namely an unstable system, complaint categorization is slow, and difficult to find solutions. This is certainly a concern for the management. In this study, the classification of complaints categories in Bukalapak will be carried out. This study aims to find out what classification model is suitable to be used in determining the category of complaints in Bukalapak. The classification model that will be used in this research is Logistic Regression, k Nearest Neighbor, and Support Vector Machine. While the data that will be used is data on complaints from January 2021 to December 2021. From the results of the study, it was found that the logistic regression classification model had the highest value among the other 2 models. The logistic regression model managed to get an accuracy value of 83.9%. The second position is occupied by the k Nearest Neighbors model with an accuracy of 77.6%. Last occupied by the SVM model with an accuracy value of 40.5%.
Improvement of Urinary Stone Segmentation Using GAN-Based Urinary Stones Inpainting Augmentation

A urinary stone is a type of abnormality that occurs frequently in the urinary system.An automated segmentation of urinary stones is important for assisting medical doctors in early diagnosis and further treatment.While deep learning techniques are effective for image segmentation, they require a large number of datasets to achieve high accuracy.We proposed a GAN-based augmentation technique for creating synthetic images based on stone and non-stone mask inputs in order to improve the segmentation network's performance by increasing the number and diversity of training data.The synthetic training images were generated from stone-contained images and stone-free images using existing stone ground truth and corresponding stone location maps, respectively.To segment urinary stones from full abdominal x-ray images, we trained the MultiResUnet model using both original stone-contained and our proposed synthetic samples.The proposed method obtained a 69.59% pixel-wise F 1 score and a 68.14% region-wise F 1 score, which achieved an improvement of 2.12% and 2.13%, respectively, over a model trained with only the original stone-contained dataset.
Integrating XR and Tangible Model to Enhance Old Building Renovation Design

The renovation of old buildings usually has complex site constraints, involves multiple interests, and has limited capital costs.Therefore, the transformation process has always encouraged the participation of stakeholders to improve the effectiveness of the design.Tangible user interfaces (TUIs) consisting of physical models further simplify the operation.However, most designs are displayed by projection, which cannot provide a realistic interactive experience.Extended Reality (XR) technology has the advantages of interactivity and clear visualization.We developed a participatory renovation tool using XR technology and tangible models.We invited the owner to view the 3D design proposal in HoloLens and then use the tangible model to discuss the renovation design.This experiment proves that the system can realize on-site 3D visualization, allowing the public to design in a real environment and intuitively interact with the virtual and the real.Using the participatory design method of XR technology + tangible model will provide a convenient platform for the renovation of old buildings with the collaborative participation of stakeholders.
Fall Detection using Biometric Information Based on Multi-Horizon Forecasting

With the steady growth of the aging population, there has been increasing interest in the health of elderly people. In particular, a physical fall can be fatal for the elderly or result in complications that cause more serious physical and mental damage than the cause of the falls. For this reason, it is important to accurately predict the occurrence of abnormal behavior in advance. Although existing methods can be used to analyze general behavioral characteristics, personalized and customized behavioral prediction remains challenging. In this paper, we propose a customized pre-prediction method using sensor wearers’ biometric information as well as behavioral data reflecting the wearers’ unique behavioral characteristics. The technique is based on a temporal fusion transformers method [1] that can be effectively applied to multi-horizon forecasting. A performance evaluation was made to compare the effects of including and excluding biometric information to quantitatively evaluate the enhancement achieved by extending the prediction model. The use of additional biometric information enabled superior performance with 0.9702, 0.9702, 0.9702, 0.9883 in F-1 score, precision, recall respectively, which is all evaluation indicators. The code for the experiments can be found at the following GitHub address: https://github.com/IKKIM00/Fall_Detection_using_multihorizon_forecasting.
Flexible FOND HTN Planning: A Complexity Analysis

Hierarchical Task Network (HTN) planning is an expressive planning formalism that has often been advocated to address real-world problems. Yet few extensions exist that can deal with the many challenges encountered in the real world, one being the capability to express uncertainty. Recently, a new HTN formalism for fully observable nondeterministic problems was proposed and studied theoretically. In this paper, we lay out limitations of that formalism and propose an alternative definition, which addresses and resolves such limitations. We also study its complexity for certain problems.
Using Technology-Based Media for Teaching Speaking in Intercultural Education

The research aimed to study how did mobile-phone were used for teaching speaking in intercultural education? and was the use of mobile phone for teaching speaking in intercultural education effective? Intercultural competence could be seen in the teaching of speaking since the students had to reflect their ideas and opinions orally. Thus, the speaking ability would be much supported by intercultural education. To reach this, the use of technology-based media like mobile-phone in teaching speaking was needed to make it easier and more interesting. In the past decade, several studies have sought to determine the use of technology in education; however, in the university context, the issues of mobile phone used for teaching speaking in intercultural education had not been discussed extensively. The research employed explanatory sequential mix-method research. The instrument of the research was an observation, questionnaire, and test. The research population was 40 students of the fifth semester of the English Education Program of Purworejo Muhammadiyah University. The finding shows that mobile phone is used as the primary tool to access the material and learning platform. Using mobile phones, the student’s speaking ability is improved by 18,3%. In addition, the finding shows that the use of mobile phones for teaching speaking in intercultural education is effective to help the teacher in delivering the material and boosting the students’ motivation to speak.