Reviewerly/exHarmony huggingface dataset:
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation

Injecting textual information into knowledge graph (KG) entity representations has been a worthwhile expedition in terms of improving performance in KG oriented tasks within the NLP community. External knowledge often adopted to enhance KG embeddings ranges from semantically rich lexical dependency parsed features to a set of relevant key words to entire text descriptions supplied from an external corpus such as wikipedia and many more. Despite the gains this innovation (Text-enhanced KG embeddings) has made, the proposal in this work suggests that it can be improved even further. Instead of using a single text description (which would not sufficiently represent an entity because of the inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a set of text descriptions relevant to KG entities as well as align or augment KG embeddings with text descriptions. Different from prior work that plugs formal entity descriptions declared in knowledge bases, this framework leverages a retriever model to selectively identify richer or highly relevant text descriptions to use in augmenting entities. Furthermore, the framework treats the number of descriptions to use in augmentation process as a parameter, which allows the flexibility of enumerating across several numbers before identifying an appropriate number. Experiment results for Link Prediction demonstrate a 5.5% and 3.5% percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10 scores respectively, in comparison to text-enhanced knowledge graph augmentation methods using traditional CNNs.
Pengembangan Media Pembelajaran Menggunakan Adobe Flash Pada Mata Kuliah Kelistrikan Otomotif

Adobe Flash is a comprehensive animation creation program that makes it easy for developers to create animations. Animation is a series of accurately processed images that directly produce memorable movements with sound and store a learning message. This study aims to develop learning media using Adobe Flash in automotive electrical courses. Based on the results of a survey of learning media needs analysis to students of Mechanical Engineering Education, State University of Jakarta whose data was collected using Google Form, 80% of respondents answered that the media most often used in Automotive Electrical courses was Power Point. 93.3% of respondents answered the need to develop android application-based learning media for Automotive Electrical courses. Research and Development research methods and ADDIE development model (Analysis, Design, Development, Implementation, Evaluation) are used in this study. Place of research at the State University of Jakarta for students who have completed and are taking Automotive Electrical courses. The method of data collection is a questionnaire to test the feasibility of the media. Based on the eligibility criteria by Arikunto, the validation test by material experts stated that the learning media tested obtained the "very feasible" criteria with a percentage value of 85.33%. Furthermore, the validation test by media experts stated that the learning media tested obtained the "very feasible" criteria with a percentage value of 81.33%. And the results of small group trials to 7 students get a percentage of 90.28% which is in the "very feasible" interval. While the results of field trials to 20 students get a percentage of 90.4% which is in the "very feasible" interval. Based on the data above, it can be concluded that the media developed is very feasible and can be used in learning.
Prediction of cancer recurrence based on compact graphs of whole slide images

Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients’ survival is accurately predicting recurrence status and giving appropriate treatment. Whole Slide Images (WSIs) are a common type of image data in the field of digital pathology, containing high-resolution tissue information. Furthermore, WSIs of primary tumors contain microenvironmental information directly associated with the growth of tumor cells. To effectively utilize this microenvironmental information. Firstly, we represented microenvironmental features of histopathological images as compact graphs. Secondly, this work aims to develop an enhanced lightweight graph neural network called the Adaptive Graph Clustering Network (AGCNet) for predicting cancer recurrence. Experiments are conducted on three cancer datasets from The Cancer Genome Atlas (TCGA), and AGCNet achieved an accuracy of 81.81% in BLCA, 69.66% in PAAD, and 81.96% in STAD. These results indicated that AGCNet is an effective model for predicting cancer recurrence and is expected to be applied in clinical applications.
Research on Aircraft image recognition based on transfer learning and improved YOLOv5 model

Effective differentiation of aircraft types using images is important for providing military combat information as well as civilian aircraft operations. Aircraft image recognition has many difficulties such as large variations of target scale, complex backgrounds, and difficult data set acquisition, which lead to the low recognition accuracy of existing models. To address the problem of low recognition accuracy caused by the above difficulties. This paper proposes the improved YOLOv5 model for the recognition of aircraft images. First, this paper designs the CSPResNet50dCA network as the backbone of the YOLOv5 model to enhance the feature extraction capability for small target aircraft in images. By introducing the coordinate attention mechanism and the CSP structure, the feature-focusing capability and the computing speed of the model are enhanced. Afterward, we use data enhancement to expand the data set and transfer learning to improve the generalization ability and convergence speed of the model, so as to improve its robustness. The experimental results show that the improved YOLOv5 model has significantly improved the recognition accuracy of aircraft targets， and significantly enhanced feature extraction ability for small target aircraft with good generalization ability.
FSASA: Sequential recommendation based on fusing session-aware models and self-attention networks

The recommendation system can alleviate the problem of ?information overload?, tap the potential value of data, push personalized information to users in need, and improve information utilization. Sequence recommendation has become a hot research direction because of its practicality and high precision. Deep Neural Networks (DNN) have the natural advantage of capturing comprehensive relations among different entities, thus almost occupying a dominant position in sequence recommendation in the past few years. However, as Deep Learning (DL)-based methods are widely used to model local preferences under user behavior sequences, the global preference modeling of users is often underestimated, and usually, only some simple and crude user latent representations are introduced. Therefore, this paper proposes a sequential recommendation based on Fusing Session-Aware models and Self-Attention networks (FSASA). Specifically, we use the Self-Attentive Sequential Recommendation (SASRec) model as a global representation learning module to capture long-term preferences under user behavior sequences and further propose an improved session-aware sequential recommendation model as a local learning representation module from user model the user?s dynamic preferences in the historical behavior, and finally use the Gated Recurrent Unit (GRU) module to calculate their weights. Experiments on three widely used recommendation atasets show that FSASA outperforms state-of-the-art baselines on two commonly used metrics.
Face mask wearing image dataset: A comprehensive benchmark for image-based face mask detection models

The Face Mask Wearing Image Dataset is a comprehensive collection of images aimed at facilitating research in the domain of face mask detection and classification. This dataset consists of 24916 images, carefully categorized into two main folders: "Correct" and "Incorrect" representing instances of face masks being worn properly and improperly, respectively. Each folder is further divided into four subfolders, each denoting a specific type of face mask - Bandana, Cotton, N95, and Surgical. In the "Correct" folder, images depict individuals correctly wearing their respective face masks, while the "Incorrect" folder contains images of improper face mask usage. To capture variations in face mask application across different demographics, such as age and gender, each subfolder also includes three additional subfolders - Child, Male, and Female. The dataset's diverse content encompasses different face mask types, covering bandana-style, cloth, N95 respirators, and surgical masks, across various age groups and genders. This design ensures a comprehensive representation of real-world scenarios, enabling the evaluation of machine learning algorithms for face mask detection and classification. Researchers can leverage this dataset to develop and assess models that can accurately identify and distinguish between correct and incorrect face mask usage. By contributing to the advancement of face mask detection technologies, this dataset further supports public health initiatives and encourages proper mask-wearing behaviour to mitigate the spread of infectious diseases, particularly during times of heightened health concerns such as the COVID-19 pandemic.
Effect of Using Picture Stories on Students’ Reading Comprehension in Narrative Text at the Ninth Grade Students of SMP Negeri 10 Pematangsiantar

This research aims to determine whether the use of picture books has a big impact on students' ability to read narrative texts. At SMP Negeri 10 Pematangsiantar, researchers conducted this research using quantitative methods and a quasi-experimental design approach. The population of this study was 201 class IX students. The instruments used to collect data are based on students’ pre-and post-test results. Based on research findings, the average score of the experimental class on the pre-test and post-test was 54 and 75, respectively. The average score of the control group was 58.65 on the pre-test and 65.57 on the post-test as well. The t-test results show (3.7 &gt; 1.677) that the t-test value is greater than the t-table value. H0, the null hypothesis, is refuted. Keeping this in mind, it can be said that picture books play a major role in helping students improve their reading skills, especially in narrative texts.
Privacy-preserving techniques for decentralized and secure machine learning in drug discovery

Data availability, data security, and privacy concerns often hamper optimal performance efficiency of machine learning (ML) techniques. Therefore, novel techniques for the utilization of private/sensitive data in the field of drug discovery have been proposed for ML model-building tasks. Some examples of the different techniques are secure multiparty computation, distributed deep learning, homomorphic encryption, blockchain-based peer-to-peer networking, differential privacy, and federated learning, as well as combinations of such techniques. In this paper, we present an overview of these techniques for decentralized ML to illustrate its benefits and drawbacks in the field of drug discovery.
Penerapan Metode Tartili Daiam Pembelajaran Membaca Al-Qur’an Di TPQ Nurul Hikmah Kertonagoro Jenggawah Jember

Abstract: The purpose of this study is to describe the implementation of the Tartili method in learning to read the Qur'an at TPQ Nurul Hikmah Kertonegoro Jenggawah Jember. This thesis is a descriptive qualitative field research, namely a method that describes the results of research in the field by means of objective rational thinking, therefore the data is collected through observation, interviews, and documentation, while the data analysis uses data reduction. The general conclusion in writing this thesis is that the implementation of the Tartili method in TPQ Nurul Hikmah Kertonegoro Jenggawah Jember has produced children who are able to read the Koran fluently, well and correctly, and students are able to understand the rules of reading the Koran that is read. Keywords: Tartili Method, al-Qur’an Learning
Early child health in Africa: do ICT and democracy matter?

Abstract This article examines the effect of information and communication technologies (ICT) and democracy on early child health using data from 51 African countries. We first specify and estimate a panel data model using ordinary least squares and two-stage least squares over the period 2001–2019. We apply the Hodrick–Prescott filter before analysis. Our results show that the extension of mobile phone use significantly contributes to the improvement of early child health in Africa. This effect is indifferent to the state or the level of democracy. Also, the internet diffusion plays a positive role in early child health when the democracy environment improves and becomes better. We suggest policies in favour of a large access to ICT tools and internet infrastructure as well as the promotion of democracy in Africa to better prevent infant mortality.
Visualization in the Wild

Visualization approaches have been successfully applied to a variety of application fields. Continuously published novel visualization approaches reflect the need for and success of visualization approaches. However, when looking at visualization techniques that designers apply in real-world applications targeted at a larger user group (e.g., medical experts, engineers, journalists, and public authorities), novel, published approaches hardly end up being used in real-world scenarios. The lack of readily available visualization software solutions and guidelines on applying these is unfortunate, especially since data science and data analytics are emerging fields, with many users needing data visualization. As shown in the different use-case scenarios outlined in this issue, implementing novel solutions within existing domain workflows is still a significant challenge for visualization researchers and domain experts. Challenges and guidelines for integrating interactive visualization applications in existing workflows are outlined in different real-world applications presented in this issue. The papers in this issue cover various domains and data types and comprise success stories of visualization approaches in different application domains.
Structure learning of Bayesian network based on firefly hybrid optimization algorithm

Aiming at the low learning efficiency and easy to fall into local optimization of Bayesian network structure learning algorithm, a hybrid optimization algorithm of fireflies (MIC-FA) is proposed. Firstly, the undirected graph containing most correct edges is obtained by the maximum information coefficient, so as to generate the initial population of MIC-FA algorithm. Then, the position of firefly individual is updated by directional movement, random movement and mutation operation in firefly optimization algorithm. Finally, the firefly with the highest Bayesian information criterion score is the best Bayesian network structure. Experimental results show that, compared with the contrast algorithm, the accuracy and optimization efficiency of the proposed algorithm are improved.
Through the 'Scope: The Year in Micromounting 2022 Was a Year of Confusion

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSIn writing this article, I owe a debt of thanks to the following people who aided me greatly. In general, I have rewritten the information that they provided, although on occasion I have let their own words stand. Nonetheless, if there are errors or omissions, they are clearly mine. Contributing were Neil Cavanagh, Marco Ciriotti, Jim Daly, Jean-Luc Designolle, André Foucart, Jim Glen, Julian Gray, James Hagadorn, Mike Howard, Kathy Hrechka, Mark Jacobson, Christina Johnson, Michel Legros, Tom Mortimer, Robert Pecorini, Pete Richards, Frank Rosenberg, Frank Ruehlicke, David Roe, Jose Santamaria, Mike Seeds, Roy Starkey, Steve Stuart, Jocelyn Thornton, René Vandenbosch, Sue Wearden, and Al Wilkins.Additional informationNotes on contributorsQuintin WightQuintin Wight is the author of The Complete Book of Micromounting (Mineralogical Record, 1993).
Upaya Pengelola Melalui Program Pohon Geulis Dalam Meningkatkan Minat Baca Peserta Didik Program Kesetaraan Paket C

The change that non-formal education wants to achieve in community development is the growth of a learning society. One of the characteristics of a learning society is the formation of a reading society. The initial stage towards readability is reading interest. This research was conducted at PKBM Bhina Swakarya, Batujajar District, West Bandung Regency. The purpose of this study was to determine the interest in reading at PKBM Bhina Swakarya and the manager's efforts to increase reading interest in his institution. This study used qualitative descriptive method. The informants in this study amounted to 5 people, the selection of informants used data source sampling techniques with certain considerations. The data collection techniques used in this study used interview, observation, and documentation techniques. The results of this study obtained that, 1) the initial condition of students carrying out reading activities for five minutes, 2) efforts made by the manager to increase interest in reading by making interesting programs, making visits to reading parks, assigning students to read, summarize, and make interesting summary results, 3) It was found that there was an increase in reading interest after the “pohon geulis” program which was a form of management effort to increase the reading interest of students of the high school equivalence education program. The increase in reading interest occurs because the obligation of students to read is imitated from the aspects of time spent, reading frequency, and quantity of reading. The increase in reading interest is due to the implications of the implementation of the “pohon geulis” in terms of student curiosity, the need for information, and the principle of students that reading is a necessity.
Students’ Needs Analysis of Business English At ESP Class For Business Administration Department At Nommensen HKBP University

Nowadays, practically all job profession criteria demand job candidates to be proficient in the language of their preferred professional industry, particularly English. This research was conducted to examine the challenges faced by the Business Administration Department, specifically the second-semester students at Nommensen HKBP University, in using English and their need for Business English in ESP classes. It follows Hutchinson and Waters' theory, which consists of target situation needs and learning needs. Target situation needs include "necessities," "lacks," and "wants," while learning needs refer to how learners learn the language. The method used in this research is qualitative. Data was collected through a Semi-structured interview with the lecturer, as well as questionnaires given to the students via Google form. In collecting and analyzing the data, the researcher followed Miles, Huberman, &amp; Saldana's concepts are data collecting, data reduction, data display, and conclusion. The researcher also used some tables and charts to describe and calculate the data accurately. The result of this research showed that Business administration students struggle with English learning due to lack of vocabulary, mispronunciation, and incorrect sentence structure. These issues cause anxiety and hinder effective communication. To improve, students should learn to comprehend reports in curves or tables. On the other hand, Business Administration students need to master four English language skills: speaking, listening, reading, and writing. 96% prefer an English for Business class to understand its value in their professional field. Lessons should be weekly, small, and include oral presentations, group discussions, and writing.
Technology education in elementary school using the example of learning robots - development and evaluation of an in-service teacher training concept

The context of the study is the increasing digitalisation of the living environment of primary school students, which is to be introduced into primary schools according to theoretical and educational policy guidelines. In this regard, further teachertraining on digital media in classrooms are particularly relevant, on the one hand to promote teachers’ digital-related pedagogical knowledge and content knowledge (DPaCK). On the other hand, studies also reveal positive correlations among teacher training, teaching activities, and students’ learning outcomes. In-service teacher training courses with adaptive support by a trainer in particular haveproven to be effective. Against the background of various research studies on professional development of teachers, a corresponding model of tripartite learning outcomes has been established and serves as a broad theoretical framework. However, the specific relationship between in-service teacher training with adaptive support, DPaCK, and computational thinking of primary school students in the context of the German primary school subject Sachunterricht has not been sufficiently studied. Therefore, the following research questions can be derived: (1) To what extent does training with adaptive support on the topic of learning robots contribute to the development of teachers’ DPaCK? (2) Which effects can be ascertained on the students’ computational thinking in technology-related Sachunterricht? To investigate this relationship, an intervention study in a pre-post design with an experimental group, a control group, and a baseline is appropriate. As results are not yet available at this point, the present paper focuses on the presentation of the theoretical background and empirical approaches.
From Uncertainty to Action

The research aims to recalibrate digital humanities methods and tools for effectively analyzing uncertain and complex colonial historical data to extract fuzzy toponyms. Additionally, I investigate how advanced digital techniques can contribute to the development of a novel framework rooted in historicaldata from the global South. To address these inquiries, I employ a case study approach, with a particular focus on the British South India corpus, utilizing advanced programming techniques BERT NER and DeezyMatch. The primary goal of this research is to create a historical gazetteer that will be incorporated with the World Historical Gazetteer.
The transposition of the copyright in the DSM Directive into Greek law: key out-takes

Journal Article The transposition of the copyright in the DSM Directive into Greek law: key out-takes Get access Yannos Paramythiotis Yannos Paramythiotis Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, Volume 18, Issue 11, November 2023, Pages 779–786, https://doi.org/10.1093/jiplp/jpad082 Published: 31 October 2023 Article history Received: 06 September 2023 Editorial decision: 07 September 2023 Accepted: 26 September 2023 Corrected and typeset: 31 October 2023 Published: 31 October 2023
Monopoly Game as A Science Learning Media for Junior High School; A Validity Analysis

Learning media is important as a means of delivering information to students. Media is not only interesting in terms of content, but also able to adjust to the characteristics of students. This study aims to reveal the validity of science learning media in the form of a monopoly game. This research uses descriptive analysis method. Data were collected through expert assessment questionnaires (as validators). Data analysis in the form of numerical values is done quantitatively, while comments or suggestions are done qualitatively. The results showed that the media was valid. Further tests were carried out, to assess the effectiveness of the media, so as to reveal more feasibility of the media.