Reviewerly/exHarmony huggingface dataset:
Brave New Lab World - Electronic Lab Books Digitise Laboratory Documentation

At German universities and universities of applied sciences, a new way of working is currently opening up in the area of the structure and composition of laboratory work: Documentation by means of electronic laboratory notebooks (ELN). Researchers produce and use a colourful mixture of data such as graphic files, formulas, tables or microscopy files. At Heinrich Heine University Düsseldorf (HHU), advice and support for researchers in the field of research data management (RDM) is provided on a regular basis. In addition to assistance with applications, we also offer support using various RDM tools, including electronic lab books. Why it makes sense to use electronic lab books, how our path with them has progressed so far, and what are the current hurdles to be overcome are presented in this report.
Virtual Reality Revolutionizing Digital Marketing Design and Optimization of Online English Teaching in Universities with Wireless Network Technology Support in the Context of 5G

In order to improve the risk prediction and management effect of ecommerce supply chain, this paper combines big data technology to carry out quantitative risk prediction and management of e-commerce supply chain, and proposes a bitemporal database middleware indexing algorithm.Moreover, this paper deals with e-commerce risk data, draws lessons from the 4R tree index method, and eliminates bitemporal variables through effective data transformation and query transformation.In addition, this paper analyzes and optimizes the two sub-processes of the original node insertion algorithm of the QR tree, and proposes the corresponding query transformation method after data transformation.Through simulation experiments, it can be seen that the risk management system of ecommerce supply chain based on big data technology proposed in this paper has good risk prediction and risk management effects.
Retracted: Investigation on Deep Learning Model of College English Based on Multimodal Learning Method

[This retracts the article DOI: 10.1155/2022/7001392.].
Progressive Literacy Task: Design Strategy of digital literacy and competencies in learning outcomes

Raising interest in reading and digital literacy skills is currently a big challenge for teachers amid the many information vortices at this time. Attention and awareness of reading are still very low among students. This study aims to make the right learning strategy to form a culture of literacy among college students. This research is descriptive research to find the right model for teaching students about digital literacy both conceptually and in skills. The data in this study is to reveal (1) student learning motivation, (2) the use of learning strategies, (3) digital literacy skills, (4) and the formation of a literacy culture. Data collection techniques in descriptive research are through direct observation, questionnaires, independent assignments, and performance. based on a collection of various data used to formulate appropriate learning strategy designs for students completing instructional technology courses .
THE ROLE OF DIGITAL TECHNOLOGY IN SUPPORTING STUDENTS’ LISTENING SKILLS THROUGH DIGITAL MUSIC PLATFORMS AND PODCASTS: A LITERATURE REVIEW

The collaboration of Digital Technology and learning has become one great topic recently. Many people try to search for good ways to support their teaching and learning process through digital technology. Technology has offered many kinds of applications that can help students and teachers. The Music platforms and podcasts consist of many things that can help the students improve their English, especially their English skills. In this literature review, I would like to find and share the role of Digital Technology in supporting students’ Listening through music platforms and podcasts. And the methodology that I would like to use is the qualitative method in which there will be some interviews and videos in collecting the data. And reading through all the results of the interviews and drawing some connections between digital technology and the learning process to analyze the data, the participants are from X Grade students of Makassar Adventists Senior High School. As a result, those students who are active with digital technology, especially the music platforms and podcasts, have better listening skills than those who are not. Here the writer found out that digital music platforms and podcasts can support students’ listening skills.
LEARN TO SPEAK ENGLISH SOFTWARE THE PERSPECTIVE AND RESPONSE (AN EXPERIMENTAL STUDY AT POLITEKNIK LP3I MAKASSAR)

The objectives of this research were: to find out whether) to find out whether or not the students are interested in joining the speaking class through Learn to Speak English software. This research employed Quasi Experimental design. The sample consisted of 32 students of third semester of business administration, Politeknik LP3I Makassar in academic year 2019/2020. The data were collected through and data on the students’ interest were analyzed using Likert scale, the results of the research were: the use of Learn to Speak English software increased the students’ interest in joining the speaking class, it can be concluded the students have high interest and active toward teaching speaking through Learn to Speak English software.
AN ANALYSIS THE USE OF KAHOOT MEDIA IN THE COOPERATIVE GRAMMAR LEARNING PROCESS AT SCHOOL

This study aimed to analyze the use of Kahoot media in cooperative grammar learning through the students' learning process at school. Data collection techniques used literature study. Researchers took seven articles and then analyzed them qualitatively. The results showed that the use of Kahoot media in the cooperative grammar learning process has a positive impact on students. Students continued to be ambitious in learning even before learning began. Students' grades were better at capability. They performed fewer mistakes and were more enthusiastic. The learning condition was fun and effective in motivating students in learning, especially learning foreign languages. Therefore, it is suggested to use Kahoot as a medium of the teaching-learning process.
k-Anonymity on Metagenomic Features in Microbiome Databases

The human microbiome is increasingly subject to extensive research, due to its relations to health, diet, exercise and illness. While ever more microbiome data is gathered and stored, recent works have demonstrated the threat of individual re-identification based on matching samples taken at different points in time, by matching metagenomic features extracted from microbiome readings. The individual and temporal stability of the microbiome varies for different body sites and is particularly pronounced for readings from the gastrointestinal tract. To meet the resulting need for privacy-protecting solutions, we adapt the well-known concept of k-anonymity and make it suitable for application to microbiome datasets. In particular, our approach for establishing k-anonymity is based on micro-aggregation.Our evaluation uses ten datasets containing samples of gut microbiomes, and analyzes the decreased privacy risk on the anonymised dataset as well as the incurred information loss. The analysis demonstrates the suitability of our approach for the protection of sensitive microbiome data.
STUDENTS’ PERCEPTIONS IN TEACHING PARAGRAPH WRITING MATERIALS BASED ON EGRA TECHNIQUE

Paragraph writing is one of subject of study at university level but there are no books provide to teach this subject. Therefore, the researcher construct teh material for Pargraph Writing based on EGRA Technique. This research is to find out the students’ perceptions on paragraph writing material based on EGRA technique. The researcher applied the descriptive qualitative research. This research was conducted in English Education Department, Faculty of Education and Teacher Training of Muhammadiyah University of Makassar. The subjects of the research were the fourth semester students in academic year of 2018/2019. The questionnaires were given to the students as the instrument to find out the students’ perception on paragraph writing materials. The are several questions that are filled out by the students. The result of the students’ perception on the final draft of instructional paragraph writing materials based on EGRA technique was positive
Evaluating Statistical Disclosure Attacks and Countermeasures for Anonymous Voice Calls

Assuming a threat model of a global observer, statistical disclosure attacks have been proposed to efficiently de-anonymize communication relationships in text-based mix networks over time. It is commonly assumed that such attacks are also able to disclose call relationships in anonymous communication networks (ACNs) that support voice calls. One straightforward countermeasure is to expect users to permanently send and receive packets that mimic a Voice over IP (VoIP) call. However, this is not practical in real world scenarios, like on mobile devices. In this article, we adapt one specific statistical disclosure attack (Z-SDA-MD) to voice calls and quantitatively study less resource-intensive countermeasures. As base countermeasure, we evaluate a round-based communication model, corresponding to a timed mix. A simulation study of this scenario shows that the Z-SDA-MD is not well suited for a general disclosure of call relationships because of too many false positives. Nevertheless, the attack is able to correctly identify the most frequent relationships. Still, the accuracy in that regard may significantly be decreased by increasing the duration of one round, by decoupling actions (call setup and teardown) of caller and callee by a random number of rounds, and by occasional fake calls to a fixed set of “fake friends”. Overall, our study shows that anonymous voice calls may be implemented with an acceptable trade-off between anonymity, call setup time, and bandwidth overhead.
Retracted: Application of Psychological Contract Theory in Mental Health and Professional Development of University Teachers

[This retracts the article DOI: 10.1155/2022/6985766.].
Retracted: Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

[This retracts the article DOI: 10.1155/2022/5435207.].
Retracted: Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques

[This retracts the article DOI: 10.1155/2022/6395860.].
Retracted: Artificial Bee Colony Based Gabor Parameters Optimizer (ABC-GPO) for Modulation Classification

[This retracts the article DOI: 10.1155/2022/9464633.].
Retracted: Clinical Treatment and Nursing Intervention Study of Clipping Treatment of Cerebral Aneurysm under the Health Model of Data Analysis

[This retracts the article DOI: 10.1155/2022/8178963.].
Retracted: Prenatal Monitoring of Perinatal Pregnant Women and Fetus Based on a Smart Electronic Fetal Monitoring System

[This retracts the article DOI: 10.1155/2022/5073636.].
Retracted: Image Segmentation Technology Based on Attention Mechanism and ENet

[This retracts the article DOI: 10.1155/2022/9873777.].
Retracted: Big Data-Driven Vocational Undergraduate Talent Training and Social Needs Adaptability Analysis and Collaborative Path Mining

[This retracts the article DOI: 10.1155/2022/8476412.].
Retracted: Customer Experience towards the Product during a Coronavirus Outbreak

[This retracts the article DOI: 10.1155/2022/4279346.].
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS WEB GOOGLE SITES PADA PEMBELAJARAN BAHASA INDONESIA KELAS IV SDN 07 SITIUNG

This research is motivated by the use of Google Sites web-based learning media which is not optimally taught to students. This study aims to produce Google Sites Web-Based learning media in Class IV Indonesian Language Learning at SDN 07 Sitiung that is valid, practical and effective. This type of research is research and development (Research and Development). The development model used is the ADDIE model. This research was conducted at SDN 07 Sitiung. The research subjects were fourth grade elementary school students. Validity test research data obtained through expert validation sheets. Practicality data was collected through teacher response questionnaires and student response questionnaires. Effectiveness data was tested through test questions. The collected data were analyzed based on the research results.Based on the results of the 5 validity test from 4 expert validators obtained a content validation score of 83%, a language validation score of 90%, a graphic validation score of 84%, a question validation score of 86% and an RPP validation score of 88%. So that the average percentage data obtained is 86.2% with a very valid category. The results of the teacher's response practicality test obtained 86% results, while the results of the student response questionnaire obtained 79.25% results in the very practical category. The results of the effectiveness test get an average percentage of 78.25% in the very effective category. Based on these results, it can be concluded that the google sites web-based learning media for learning Indonesian at SDN 07 Sitiung which was developed can be declared valid, practical and effective.
PENGEMBANGAN E –LKPD BERBASIS APLIKASI QUIZIZ PADA MUATAN IPA MATERI EKOSISTEM DI KELAS V SD NEGERI 149/VIII MUARA TEBO KABUPATEN TEBO

This research is motivated by the low scor in Natural science of students and the previous use of thematic books es a learning media, especially in the subjet Ecosystem, therefore, there is a need for an improvent in the learning process to anhance student’s learning outcomes by using a teaching media the can make student’s active and interactive during the learning proscess. This research adopts the ADDIE model. The ADDIE model consists of five stages of Developmen: Analyze, Desing, Development, Implementation, and evaluation. The first stage is the Analyze stage, where the need for development is analyze,the nex stage is the where a product to development is desinged. The com development stage, aimed t, producing the privously desingd product. The implementation stage follows, where devlopment productsis implemented. Lastly, the evaluation stage involves evaluating, the development product. The assesment results of the quizizz learning media, validated by there validators, yielded an average score 96% categorized as hing valid . of the quizizz learning media is considered by teachers resultes a precentage 90,5%, categorized as hinghly practical in terrms of ase of use anf time efficiency. The particlity tes involving 32 students who used the development fot topic 1 showed a completion rate 87,2% students, topic 2 had a completion rate of 94,4% had a complection rate of 94,2% media was categori as hingky pratical in trems of ase of use and time effcienci. It was also deemed hingly effectife.
PENGARUH PENGGUNAAN MEDIA SCRAPBOOK TERHADAP MOTIVASI BELAJAR IPS KELAS IV SDN

Media scrapbook which packaged in book form by combining various pieces of images as well as explanations that are expected to attract the attention and activity of students This study aims to determine whether there is an influence of the use of scrapbook media on the motivation to learn social studies grade IV SD Negeri 22 Palembang. This research design is a pre-experimental research with the design of The One Group Pretest-Posttest design. The population of this study was the entire class IV of SDN 22 Palembang which amounted to 24 people. The sample is determined by random sampling technique. The sample of this study was students conducted using test methods in the form of multiple choice and questionnaires measuring learning motivation. The results showed that there was an influence of the use of scrapbook media on the motivation to learn social studies SDN 22 Palembang. This is shown from the paired test results of the T-test sample, pretest data and posttest learning motivation get a signification value of 0.819 &lt; 0.005, then Ho is rejected and Ha is accepted. &#x0D; Keywords: Media, Scrapbook, Learning motivation
Retracted: Privacy-Preserving Sports Wearable Data Fusion Framework

[This retracts the article DOI: 10.1155/2022/6131971.].
Retracted: Application of a FL Time Series Building Model in Mobile Network Interaction Anomaly Detection in the Internet of Things Environment

[This retracts the article DOI: 10.1155/2022/2760966.].
Multi-Label Classification of Indonesian Online Toxicity using BERT and RoBERTa

Online toxicity detection in Indonesian digital interactions poses a significant challenge due to the complexity and nuances of language. This study aims to evaluate the effectiveness of the BERT and RoBERTa language models, specifically IndoBERTweet, IndoBERT, and Indonesian RoBERTa, for identifying toxic content in Bahasa Indonesia. Our research methodology includes data collection, dataset pre-processing, data annotation, and model fine-tuning for multi-label classification tasks. The model performance is assessed using macro average of precision, recall, and F1-score. Our findings show that IndoBERTweet, fine-tuned under optimal hyperparameters (5e-5 learning rate, a batch size of 32, and three epochs), outperforms the other models with a precision of 0.85, recall of 0.94, and an F1-score of 0.89. These findings indicate that IndoBERTweet performs better in detecting and classifying online toxicity in Bahasa Indonesia. The study ’s implications extend to fostering a safer and healthier online environment for Indonesian users, while also providing a foundation for future research exploring additional models, hyperparameter optimizations, and techniques for enhancing toxicity detection and classification in the Indonesian language.
SDL-Net: A Combined CNN &amp; RNN Human Activity Recognition Model

Human Action Recognition is quite popular among researchers and scientists and is considered one of the most active applications in the field of computer vision. It is quite useful in modern era applications like healthcare, surveillance, sports and many more fields. Deep Learning has provided an upliftment to predict human actions in an easiest way possible. This paper proposes a combined CNN & RNN human action recognition model named SDL-Net, which generates skeletal representations using Part Affinity Fields (PAFs) and generates skeletal gait energy images. It also captures sequential patterns to generate sequential data as well. Experiments are conducted on Kinect Activity Recognition Dataset (KARD) and it shows the efficiency and effectiveness by achieving desirable results.
Decluttering the Internet with Article Summarization, Classification, and Recommendation using Natural Language Processing

The explosion of information available on the internet has led to an overwhelming amount of data that is available to users. The ability to efficiently consume and comprehend this information is crucial for productivity and knowledge expansion. However, only a few of researches focus on providing e-readers not only with comprehensive classification but also relevant recommendations based on the reader's history. The paper has been implemented in pursuit of facilitating the consumption of information with increased coherency and efficiency, leading to a proliferation in productivity in the fields of both professional and informal research using an easy-to-use web-based dashboard, the project works in 3 broad tenets. BERT Algorithm (Bidirectional Encoder Representations from Transformers) for information summarization, followed by Latent Dirichlet allocation (LDA) Algorithm for text classification and Collaborative filtering for recommending further articles for the user's knowledge expansion.
Dialogue Generation Conditional on Predefined Stories: Preliminary Results

This paper introduces dialogue generation conditional on predefined stories, which will be called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Story2Dialogue</i> . As a starting point for the task, this paper presents benchmark performances using simple but modern baseline methods along with error analysis. The experimental results show that few-shot prompting using large-scale pre-trained language models outperforms human effort in some objective evaluation metrics, but the quality of the generated dialogues is far inferior to humans’ creations in terms of suitability as entertainment content and semantic equivalence to the input story. Regarding suitability as a movie script, human evaluators preferred automatically generated dialogues over those created by human writers in only 20% to 29% of cases. As for semantic equivalence to the input story, 75% to 80% of the automatically generated dialogues were found to be semantically insufficient. The error analysis shows that around 80% of the semantically insufficient dialogues lacked information from the given stories and, conversely, irrelevant utterances (including undefined continuations of conversations) were added in 20% to 26% of the dialogues.
Mutually Adaptable Learning

In real-world applications that involve complex data dependencies, it would be essential to proceed with machine learning tasks in an adaptable manner. This article presents a novel Mutually Adaptable Learning (MAL) approach that allows for, on the one hand, extracting the most crucial information from data, and on the other hand, maximally utilizing it through model learning, in a mutually adaptable manner. We elaborate our MAL approach by explaining how it determines the necessity for the adaptation of both features and the learning model, integratively adapts between feature selection and model learning, and optimally achieves the learning objective. To systematically validate the effectiveness of MAL, we conduct comprehensive experiments on challenging learning tasks from two representative domains: spatiotemporal prediction and chaotic behavioral prediction, where the complex data dependencies are general encountered. Results demonstrate that MAL outperforms existing learning methods. Moreover, we show that the formulated objective can be attained under an information-theoretic guarantee. With both empirical and theoretical supports, MAL offers an effective solution to the problem of feature and model adaptation to achieve desired learning objective for given complex tasks.