Reviewerly/exHarmony huggingface dataset:
PENGGUNAAN KALIMAT INTEROGATIF PADA ANAK USIA 5 TAHUN: KAJIAN PERKEMBANGAN SINTAKSIS

The purpose of this research is to describe the forms of interrogatives used in TK Aisyiyah 56, South Tangerang City. The method used in this research is a qualitative descriptive method. The subjects of this research are 5-year-old children from TK Aisyiyah 56, South Tangerang City. The data collected in this research are speech acts in the form of questions from 5-year-old children. The data collection techniques used in this research are observation and literature review. The researcher is the instrument in this research, analyzing the data obtained from direct observation and interaction with the research subjects. The results of this research show that the types of interrogatives used by 5-year-old children in TK Aisyiyah 56, South Tangerang City consist of the interrogative words 'apa' (what), 'siapa' (who), 'kapan' (when), 'di mana' (where), 'gimana' (how), and 'kenapa' (why). The interrogative sentences with the interrogative word 'apa' were found in 9 data, 'siapa' in 8 data, 'gimana' in 2 data, 'kenapa' in 3 data, 'kapan' in 4 data, and 'di mana' in 4 data. From the data, the most dominant interrogative speech acts used were those using the interrogative words 'apa' and 'siapa'
Towards a Verification Approach of a Smart Registry for Chronic Heart Failure Patients

The daily increasing amount of health data from different sources like electronic medical records and telehealth systems go hand in hand with the ongoing development of novel digital and data-driven analytics. Unifying this in a privacy-preserving data aggregation infrastructure can enable services for clinical decision support in personalized patient therapy.The goal of this work was to consider such an infrastructure, implemented in a smart registry for heart failure, as a comparative method for the analysis of health data.We analyzed to what extent the dataset of a study on the telehealth program HerzMobil Tirol (HMT) can be reproduced with the data from the smart registry.A table with 96 variables for 251 patients of the HMT publication could theoretically be replicated from the smart registry for 248 patients with 80 variables. The smart registry contained the tables to reproduce a large part of the information, especially the core statements of the HMT publication.Our results show how such an infrastructure can enable efficient analysis of health data, and thus take a further step towards personalized health care.
Prediction of heart attack risk using linear discriminant analysis methods

Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart attack, the risk of permanent damage increases with every second the heart tissue cannot receive enough blood. If early and appropriate intervention is not performed, loss of heart tissue occurs. Causes such as smoking, cholesterol, diabetes, high blood pressure, old age, obesity, genetics, and high levels of certain substances produced in the liver are the main risk factors for heart attack. This study aims to predict the risk of heart attack with machine learning methods using a dataset created by considering risk factors. Methods: The performances of three types of Linear Discriminant Analysis classifiers, Normal, Ledoit-Wolf, and Oracle Shrinkage Approximating, were compared on the Cleveland dataset. Results: Normal Linear Discriminant Analysis made the best classification with 83.60% accuracy and performed better than regularized versions. Conclusion: Linear Discriminant Analysis methods are a promising classifier for heart attack prediction and can be applied in hospitals as an objective and automated system that eases specialists' workload and helps reduce diagnostic costs.
Investigation of financial applications with blockchain technology

Aims: This article investigates recent advancements in machine learning and blockchain technology for cryptocurrency price prediction. The study presents a ML system using various techniques applied to six different datasets. The findings highlight that simpler models can outperform complex ones in predicting cryptocurrency prices. Methods: The methods used in this study include applying diverse ML techniques such as LSTM, CNN, SVM, KNN, XGBoost, Astro ML, LASSO, RIDGE, linear regression, DT, and GP on six cryptocurrency datasets to predict prices. Results: The research evaluated various machine learning techniques for predicting cryptocurrency prices and reported the following RMSE values: Bitcoin prediction using Nadaraya-Watson kernel regression yielded an RMSE of 0.17, while Dogecoin prediction with linear regression resulted in an RMSE of 0.032. Ethereum price prediction using Gaussian regression achieved an RMSE of 0.02. For USD Coin, a combination of XGBoost, Gaussian regression, and Ridge techniques led to an RMSE of 0.014. Binance Coin price prediction using Gaussian regression had an RMSE of 0.032, and finally, Cardano Coin prediction employing LSTM reached an RMSE of 0.059. Conclusion: This study demonstrated the effectiveness of various machine learning techniques in predicting cryptocurrency prices. It revealed that simpler models can outperform complex ones in certain cases. The research contributes valuable insights to the field and can guide future work in cryptocurrency price prediction. The proposed model achieved promising results as evaluated by the RMSE metric.
The Importance of e-Book Platforms in Education

Many people have considered purchasing a tablet device, such as an iPad, Kindle, or Nook, for personal use, yet how many have considered adopting this new technology as a pedagogical tool in the classroom? This article presents the complexity of e-book platforms from Romania in comparison with Amazon, FNAC, or Springer and how they are being used in Romanian market. Moreover, the analysis through criteria comparison will show which strategies are mostly used by students. The result of the research and analysis had shown us the fact that the number of e-books used are increasing in Romania and online platforms are using different strategies in order to promote the digitalization of print books, especially in education and professional documents. What helps people to trust e-books more are the tutorials of using or editing them
Application of Internet of Things (IoT) Technology to KWT “Sido Makmur” Oyster Mushroom Cultivation

Ngaran Hamlet, an area in Gilangharjo Village, Pandak District, Bantul Regency, DIY, has a Women Farmers Group (KWT), "Sido Makmur," which is engaged in oyster mushroom cultivation. One of the challenges KWT "Sido Makmur" faces is the fluctuation and instability of oyster mushroom production due to environmental conditions around the mushroom barn, which are not always ideal. To overcome this, the Community Service Team from Universitas Muhammadiyah Yogyakarta (UMY), in collaboration with Singapore Polytechnic (SP), carried out Community Service activities under the Community Partnership (PKM) scheme. This service aims to assist KWT "Sido Makmur" in facing the challenges of oyster mushroom production by designing and implementing Appropriate Technology in the form of a temperature and humidity monitoring system in mushroom barns based on the Internet of Things (IoT). The service team's programs and activities include planning, designing, implementing, testing, and analyzing the IoT technology-based mushroom monitoring system for temperature and humidity. This system allows oyster mushroom farmers in Ngaran Hamlet to monitor the condition of the mushroom coop in real-time and remotely via the IoT platform to increase the productivity of oyster mushrooms optimally. The results of the implementation and testing of the system show that the monitoring tool for temperature and humidity in the IoT-based mushroom barn has been proven to function properly in accordance with the design planning and expectations of oyster mushroom farmers in Ngaran Hamlet.
Using English Songs to Teach Vocabulary in an Elementary School

Despite the limited number of students, several teaching and learning activities have been conducted online and face-to-face during the pandemic. Accordingly, it becomes a challenge for elementary school teachers in their English teaching practice as students are more focused on mastering vocabulary. Therefore, teachers should create a pleasant and joyful classroom to ensure students learn English effectively. According to that background, English teaching community service programs should benefit elementary schools. This program was to help create a fun classroom and make the learning process more effective by using songs as the vocabulary teaching method. Muhammadiyah Senggotan Elementary School was involved since it claimed English as its concerned subject to attract prospective students. Therefore, there was a great challenge to provide a variety of teaching methods while doing online and face-to-face learning. This program was conducted by teaching third-grade students during the pandemic using face-to-face meetings. Four stages were applied in this program: need analysis, planning, monitoring, and evaluation. The pre-test and post-test results showed a significant difference in students' vocabulary level mastery before and after applying songs in the English teaching and learning process. Hence, this program's sustainability is maintained by increasing the variety of teaching methods through different activities.
Penggunaan Video Animasi Pembelajaran bagi Guru Sekolah Dasar

The Covid-19 (Coronavirus Disease 2019) pandemic has had some effect on the development of children's education because they have to learn independently online. Online learning is a big challenge for schools, teachers, and parents because they are required to be able to manage and design learning media to achieve learning goals and prevent student boredom during online learning. This article describes one of the learning media for elementary school students by making animated videos that can be easily understood. The goal was to provide learning methods to attract students' interest in online learning during the pandemic. This learning model emphasized visualization and summary explanation of the material. Community service partners, in this case, are teachers at SD Sumberwindu I, Tanjung Anom District, Nganjuk Regency, and 5th-grade students. Activities are carried out by identifying school problems and training in making animated videos. While using the animated video, the learning purpose will be easy to achieve. This also increases the motivation of the students to be more active in learning by their shelf and seeking new knowledge related to the video. In this community service, we introduced learning materials in the form of animated videos and uploaded them to a YouTube channel. Those videos can be accessed either by teachers or students. This learning model was well-received and well-appreciated by the teachers and students.
IMPROVING VOCABULARY USING TIK TOK APPLICATION

This research is aimed at investigating of improving vocabulary using Tik Tok Application at Mts Mambaul Ulum Gumukmas. The type of this research is classroom action research (CAR). Classroom action research is a study that consists of several stages in one cycle, namely preparing, acting, observing, and reflecting. In collecting data, researchers used three methods, namely observation, interviews, and tests. The result that can be conveyed are: (a)In the preliminary condition, the students' vocabulary ability is still below the minimum completeness criteria value with an average value of 61.19, (b) The application of video tiktok in learning in the classroom is known to have a positive effect on the development of students' vocabulary skills, in the first cycle it is known that after conducting two meetings on the final test it is known that the average score of children has increased to 75.38, and (c) The application of tiktok videos in the second cycle is known to have increased again from 75.38 in the first cycle, in the second cycle it has increased to 81.76, so it can be concluded that the application of tiktok videos has a good influence on the process and results of vocabulary learning. Keywords: Vocabulary, Tik Tok Application, and Classroom Action Research (CAR)
STRATEGI SAMUDRA PUTIH: MEMANFAATKAN ISU SAMPAH PLASTIK UNTUK MENCIPTAKAN PELUANG BISNIS PRODUK WAKAKA™

The only group representing the Sriwijaya State Buddhist College (STABN) to enter the Student Creativity Development Competition in the Entrepreneurship (PKM-K) competition is the 2022 WAKAKA™ product. Conceptualization and business implementation are raised from the issue of plastic waste. Of course, this issue is a serious problem in many developing countries, including Indonesia. Science and technology have invented ingenious tools to recycle plastic waste into economically valuable recycling materials. PKM-K WAKAKA™ works with producer partners to purchase finished products in the form of T-shirts and hoodies. This product also inspires consumers through the simplicity and wisdom of Zen in the artwork and calligraphy that is printed on the product. Products named with the WAKAKA™ trademark: Wahana Karya Wisdom develop businesses using the White Ocean Strategy. Steps are taken to proclaim or promote to save the planet by reducing plastic waste (reduce), reusing used goods (reuse) and using items made from recycled materials (recycle), Inspiring the community and the students themselves through Zen wisdom. In social steps, Buddhist entrepreneurship is practiced theoretically. Based on the literature review, the white samudara strategy business concept is suitable for product development in the PKM-K program held by the Directorate General of Buddhist Community Guidance, Ministry of Religion of the Republic of Indonesia.
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling

Diffusion-based generative graph models have been proven effective in generating high-quality small graphs. However, they need to be more scalable for generating large graphs containing thousands of nodes desiring graph statistics. In this work, we propose EDGE, a new diffusion-based generative graph model that addresses generative tasks with large graphs. To improve computation efficiency, we encourage graph sparsity by using a discrete diffusion process that randomly removes edges at each time step and finally obtains an empty graph. EDGE only focuses on a portion of nodes in the graph at each denoising step. It makes much fewer edge predictions than previous diffusion-based models. Moreover, EDGE admits explicitly modeling the node degrees of the graphs, further improving the model performance. The empirical study shows that EDGE is much more efficient than competing methods and can generate large graphs with thousands of nodes. It also outperforms baseline models in generation quality: graphs generated by our approach have more similar graph statistics to those of the training graphs.
PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models

Pre-trained Language Models such as BERT are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution to the analysis of the interplay between memorization and performance of BERT in downstream tasks. We propose PreCog, a measure for evaluating memorization from pre-training, and we analyze its correlation with the BERT's performance. Our experiments show that highly memorized examples are better classified, suggesting memorization is an essential key to success for BERT.
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification

The use of modern Natural Language Processing (NLP) techniques has shown to be beneficial for software engineering tasks, such as vulnerability detection and type inference. However, training deep NLP models requires significant computational resources. This paper explores techniques that aim at achieving the best usage of resources and available information in these models.
Autoencoder-based prediction of ICU clinical codes

Availability of diagnostic codes in Electronic Health Records (EHRs) is crucial for patient care as well as reimbursement purposes. However, entering them in the EHR is tedious, and some clinical codes may be overlooked. Given an in-complete list of clinical codes, we investigate the performance of ML methods on predicting the complete ones, and assess the added predictive value of including other clinical patient data in this task. We used the MIMIC-III dataset and frame the task of completing the clinical codes as a recommendation problem. We con-sider various autoencoder approaches plus two strong baselines; item co-occurrence and Singular Value Decomposition (SVD). Inputs are 1) a record's known clinical codes, 2) the codes plus variables. The co-occurrence-based ap-proach performed slightly better (F1 score=0.26, Mean Average Precision [MAP]=0.19) than the SVD (F1=0.24, MAP=0.18). However, the adversarial autoencoder achieved the best performance when using the codes plus variables (F1=0.32, MAP=0.25). Adversarial autoencoders performed best in terms of F1 and were equal to vanilla and denoising autoencoders in term of MAP. Using clinical variables in addition to the incomplete codes list, improves the predictive performance of the models.
Entanglement generation through Markovian feed-back in open two-qubit systems

We discuss the generation and the long-time persistence of entanglement in open two-qubit systems whose reduced dissipative dynamics is not apriori engineered but is instead subjected to filtering and Markovian feedback. In particular, we analytically study 1.) whether the latter operations may enhance the environment capability of generating entanglement at short times and 2.) whether the generated entaglement survives in the long-time regime. We show that, in the case of particularly symmetric Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) it is possible to fully control the convex set of stationary states of the two-qubit reduced dynamics, therefore the asymptotic behaviour of any initial two-qubit state. We then study the impact of a suitable class of feed-back operations on the considered dynamics.
GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning

This paper presents our contribution to the MEDIQA-2023 Dialogue2Note shared task, encompassing both subtask A and subtask B. We approach the task as a dialogue summarization problem and implement two distinct pipelines: (a) a fine-tuning of a pre-trained dialogue summarization model and GPT-3, and (b) few-shot in-context learning (ICL) using a large language model, GPT-4. Both methods achieve excellent results in terms of ROUGE-1 F1, BERTScore F1 (deberta-xlarge-mnli), and BLEURT, with scores of 0.4011, 0.7058, and 0.5421, respectively. Additionally, we predict the associated section headers using RoBERTa and SciBERT based classification models. Our team ranked fourth among all teams, while each team is allowed to submit three runs as part of their submission. We also utilize expert annotations to demonstrate that the notes generated through the ICL GPT-4 are better than all other baselines. The code for our submission is available.
Time and energy bounds of driven quantum operations for quantum computing

Quantum computers are operated by external driving fields, such as lasers, microwaves or transmission lines, that execute logical operations on multi-qubit registers, leaving the system in a pure state. However, the drive and the logical system might become correlated in such a way that, after tracing out the degrees of freedom of the driving field, the output state will not be pure. Previous works have pointed out that the resulting error scales inversely with the energy of the drive, thus imposing a limit on the energy-efficiency of quantum computing. In this study, focusing on the Jaynes-Cummings model, we show how the same scaling can be seen as a consequence of two competing phenomena: the entanglement-induced error, which grows with time, and a minimal time for computation imposed by quantum speed limits. This evidence is made possible by quantifying, at any time, the computation error via the spectral radius associated to the density operator of the logical qubit. Moreover, we also prove that, in order to attain a given target state at a chosen fidelity, it is energetically more efficient to perform a single driven evolution of the logical qubits rather than to split the computation in sub-routines, each operated by a dedicated pulse.
Kalmár-style constructive completeness proofs for classical positive propositional calculi

We shall settle the completeness of some classical positive propositional calculi (positive propositional calculi in which the so-called Peirce's law holds) by resorting to a close adaptation of Kalmar's completeness proof procedure. First of all, we shall employ this adaptation to establish the completeness of the most familiar axiomatic characterization of the classical logic of material implication and disjunction. Next, we shall demonstrate that the completeness of the so-called classical positive propositional calculus can be proved either through essentially the same adaptation of Kalmar's procedure or as an immediate consequence of the conjunction of our first result and the circumstance of each positive formula being reducible to a syntactically and semantically equivalent one exhibiting a particular normal form. Finally, by defining disjunction in terms of material implication and so mirroring the classical calculus of material implication and disjunction in the classical implicative calculus, the completeness of the latter, as well as the completeness of the classical calculus of material implication and conjunction, will be shown to follow from the completeness of the former in quite simple and direct ways. It is worth noting that all these completeness proofs will be constructive, that is to say, we can easily extract from each of them an effective method for producing formal deductions of all theses of the axiomatic system concerned.
Recommender Systems with Generative Retrieval

Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. To the best of our knowledge, this is the first Semantic ID-based generative model for recommendation tasks. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.