Reviewerly/exHarmony huggingface dataset:
Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance

Abstract Background Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. Methods We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. Results Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n &lt; 3000) and attributable to random noise and variability in estimating realized values in small samples. When learning effects were specified, synthetic datasets exhibited changes in the probability of an adverse outcomes as cases accrued for the treatment group impacted by learning and stable probabilities as cases accrued for the treatment group not affected by learning. Conclusions Our framework extends clinical data simulation techniques beyond generation of patient features to incorporate hierarchical learning effects. This enables the complex simulation studies required to develop and rigorously test algorithms developed to disentangle treatment safety signals from the effects of experiential learning. By supporting such efforts, this work can help identify training opportunities, avoid unwarranted restriction of access to medical advances, and hasten treatment improvements.
KEEFEKTIFAN MODEL PEMBELAJARAN FLIPPED CLASSROOM BERBANTU VIDEO PEMBELAJARAN UNTUK MENINGKATKAN PEMAHAMAN KONSEP MATEMATIKA DAN KEMAMPUAN LITERASI NUMERASI SISWA KELAS VI GUGUS KI HAJAR DEWANTARA KEC. WONOTUNGGAL

The objectives to be achieved in this study are (1) to determine the effectiveness of the flipped classroom learning model assisted by video learning in increasing students' understanding of mathematical concepts, (2) to determine the effectiveness of the flipped classroom learning model assisted by video learning in improving students' numeracy literacy skills, (3) knowing factors supporting the implementation of the flipped classroom learning model assisted by video learning in improving students' understanding of mathematical concepts and numeracy literacy skills. This type of research is quasi-experimental research with the Nonequivalent Control Group Design. The population in this study were students in grade VI semester II of Penangkan Public Elementary School and Siwatu 01 Public Elementary School in the Ki Hajar Dewantara Cluster, Wonotunggal District, for the 2022/2023 academic year, with a total of 40 students. Data were obtained through test instruments and student response questionnaires. The results of the study prove that (1) learning statistics for class VI students using the flipped classroom learning model assisted by learning videos is effective in increasing students' understanding of mathematical concepts. (2) learning statistics for class VI students using the flipped classroom learning model assisted by learning videos effectively improves students' numeracy literacy skills. (3) there are factors that support the effectiveness of the flipped classroom learning model assisted by video learning in increasing students' understanding of mathematical concepts and numeracy literacy skills including teacher-facilitated learning, delivery of learning objectives, training self-confidence, video assistance can deepen understanding of the material, discover new knowledge and can learn anywhere and anytime.
Is it Possible to Realize Consciousness Artificially?

Three identified possible directions for the artificial realization of consciousness are considered: 1) materialistic; 2) idealistic; 3) hybrid. In principle, this is the most difficult task of artificial intelligence. The most promising approaches to the artificial realization of consciousness are distinguished in the directions. As a result of the analysis, a hypothesis was formulated about the possibility of such an implementation. Moreover, this can be, in principle, carried out within the framework of the three directions noted, but approximately. At the same time, if we consider the consciousness of a particular person, then in an artificial analogue, in essence, everything that is stored in the brain of this person should be reproduced, and this is a task offantastic complexity. The author sees a possible solution of the problem by copying information from the corresponding brain. The question of the dynamic change of consciousness as a result of human interaction with the external environment will also be very difficult.
THE EFFECT OF GLYCERIN APPLICATION ON ROUGHNESS OF NANOHYBRID COMPOSITE RESIN IMMERSED IN ISOTONIC DRINK

ABSTRACTBackground: Composite resin is the most commonly used restorative material. One type of composite resin based on the filler material is a nanohybrid composite resin which has the advantage of good strength and smooth surface to produce good improvements in terms of durability and aesthetics. The roughness of the resin can be affected by drinks with low pHs, such as isotonic drink. Glycerin can reduce the roughness of the composite resin by acting as a barrier so that an oxygen inhibition layer (OIL) is not formed. Purpose: This study is to determine the effect of glycerin application on the roughness of nanohybrid composite resin immersed in an isotonic drink. Method: A total of 32 samples of nanohybrid composite resin used in this study were made with a diameter of 5 mm and a thickness of 2 mm. The nanohybrid composite resin samples were divided into group I without glycerin application and group II with glycerin application before the light curing process. Both groups were then immersed in an isotonic drink for 18 hours in an incubator. The research continued with roughness testing using a surface roughness tester. Result: There was a significant difference in the roughness of the nanohybrid composite resin immersed in an isotonic drink with and without glycerin application. This is indicated by the p-value &lt; 0.05 in the unpaired bivariate t-test, which means that there is a significant difference in the two sample groups. Conclusion: The application of glycerin can reduce the roughness of the nanohybrid composite resin immersed in isotonic drink. Keywords: Glycerin application, Nanohybrid composite resin, Roughness.
Correction to: Who’s watching? Classifying sports viewers on social live streaming services

1 The errors in section AbstractThe sentence “52,545 sports SLSSs viewers’ viewing duration time is predicted by a feedforward neural network.” should be amended to “52,754 sports SLSSs viewers’ viewing duration time is predicted by a feedforward neural network.”
Hate Speech Detection in Social Media Using the Ensemble Learning Technique

&lt;p&gt;This is a WIP (Work in progress) article to determine hate speech in social network using ML&lt;/p&gt;
THE CORRELATION BETWEEN PARENTING STYLE AND STUDENT DEF-T INDEX

ABSTRACTBackground: Children at primary school age have a special phase in their oral cavity, namely transition phase which is marked by the change of deciduous teeth to permanent teeth. Damage that occurs during this period can affect the condition of the oral cavity and cause various diseases in the future. One of the diseases that often occurs is caries. Based on Riskesdas, the prevalence of caries in Indonesia on 2018 is 57.6% and 59.6% in South Kalimantan. The incidence of caries in children cannot be separated from the parenting provided by parents. Parenting styles are divided into authoritative, authoritarian, and permissive parenting styles. Purpose: To analyze the relationship between parenting style and def-t index of students at SDN Kebun Bunga 4 Banjarmasin. Material and Method: This study used an analytical observational method with a cross-sectional approach. The research sample was 33 students in grades 1-3rd at SDN Kebun Bunga 4 Banjarmasin. Data collection included primary data obtained through direct examination of the students' oral cavity and filling out the PSDQ (Parenting style and Dimensional Questionnaire) to determine parenting styles. The data then processed and analyzed using the SPSS application with Spearman test. Results: The results showed that most parents practice authoritative parenting. The def-t index of students is 6.3 in high category, and there are no parents with authoritarian parenting. Spearman's correlation test showed there was no relationship between parenting style and students' def-t index (p value = 0.371). Conclusion: There is no relationship between parenting styles and students' def-t index.Keywords : Authoritarian, authoritative, parenting style, permissive, student def-t index
GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation

Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models; (2) interpreting user interests to improve relevance and diversity; and (3) adapting practical circumstances such as growing item inventories. To address these limitations, we present GPT4Rec, a novel and flexible generative framework inspired by search engines. It first generates hypothetical "search queries" given item titles in a user's history, and then retrieves items for recommendation by searching these queries. The framework overcomes previous limitations by learning both user and item embeddings in the language space. To well-capture user interests with different aspects and granularity for improving relevance and diversity, we propose a multi-query generation technique with beam search. The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items. With GPT-2 language model and BM25 search engine, our framework outperforms state-of-the-art methods by $75.7\%$ and $22.2\%$ in Recall@K on two public datasets. Experiments further revealed that multi-query generation with beam search improves both the diversity of retrieved items and the coverage of a user's multi-interests. The adaptiveness and interpretability of generated queries are discussed with qualitative case studies.
Research on the practice and exploration progress of data journals

With the booming trend of data publishing, data journals have carried out much extensive practice for the exploration of open data sharing. This study focuses on the publishing practice of data journals, which took representative, relatively mature and different disciplinary scopes of data journals as the research objects. The publishing volume and proportion of data papers in data journals are calculated. Based on the author's practical experience in data publishing for several years, this study explores the formulation rules and implementation of data publishing policies. The study results show that data journals generally have well-developed regulations on data use license, data storage and permanent identifier, associated access from paper to data, data review, standardized data citation, but with few practical cases of data update and less attention paid to the problem of data security and data copyright. To facilitate the further development of data journals, we proposed in this study that more operational and reference details of data publishing should be considered in the practice of data publishing in journals.
What is Cloud computing Benfits and challenges of Cloud

Cloud computing provides on-demand delivery of computer resources ranging from, compute, data storage, networking, software, and other IT services over the internet in exchange for pay as you go billing model. The study discusses various types of cloud computing available, cloud computing advantages, cloud computing implementation models, cost-effective IT solution, IT service agility, challenges, and features of cloud computing in this growing digital world.&#x0D; Keywords: cloud, data storage, compute, digital, on-demand billing model.
Web-Based Agricultural Management Products for Marketing System: Survey

The development of information systems motivated scientists to use their knowledge to improve new tactics that can provide competitive advantages in a highly complex environment, primarily in the management of agricultural products marketing systems. Farmers often blame marketing issues for their struggles. Poor prices, a lack of transportation, and substantial post-harvest losses are all issues that they can recognize, but they are typically ill-equipped to solve. Modern technology, particularly Internet and mobile connectivity, makes it easier for people to do their work across a wide range of activities, including economics, commerce, marketing, and agriculture. Agricultural Information Management Systems (AIMS) are becoming more popular as a useful sector as agriculture expands and becomes one of the world's most lucrative enterprises. In recent years, people have become increasingly dependent on this technology. Systems have evolved from passively absorbing information to actively incorporating users as an important part of the system. The goal of this research to find out whether various agricultural implementations of various systems have a good effect on their operations by facilitating chores for farmers and dealers, and merchants easier. Study 26 investigated whether farmers' and retailers' use of web-based technology facilitated the display and sale of goods.&#x0D; KEYWORDS: Agricultural Products, Marketing System, E-Commerce, E- Marketing, Agricultural Marketing.
EXAMINING THE CONTENT ALIGNMENT BETWEEN LANGUAGE CURRICULUM AND A LANGUAGE TEST IN CHINA

This study examined the content alignment between an English as a foreign language skills curriculum and a provincial language test in China. When there is misalignment in the content between the standards of a curriculum and a test, conclusions about student abilities and teaching effectiveness can be questioned. To examine this, three categories of alignment were investigated using document analysis and expert judgment: categorical concurrence, range of knowledge correspondence, and balance of representation. Eight reviewers coded the curriculum and test items. Results showed that the curriculum aligned across the three criteria for the listening and reading skills. For the writing skills, the range of knowledge correspondence and balance of representation criteria were met, but categorical concurrence was not. The test did not include speaking items, so there was complete misalignment with that curriculum. The findings showed that the test partially aligned with the curriculum, suggesting that performance may not fully represent students’ ability to meet the curricular standards. We recommend that future tests should comprehensively cover all of the content in the curriculum and when doing so to ensure there is a sufficient number of items measuring each objective. This would improve how accurately interpretations of student performance can be made.
Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch

Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation. Despite the empirical success, most self-supervised learning methods are rather "inefficient" learners, typically taking hundreds of training epochs to fully converge. In this work, we show that the key towards efficient self-supervised learning is to increase the number of crops from each image instance. Leveraging one of the state-of-the-art SSL method, we introduce a simplistic form of self-supervised learning method called Extreme-Multi-Patch Self-Supervised-Learning (EMP-SSL) that does not rely on many heuristic techniques for SSL such as weight sharing between the branches, feature-wise normalization, output quantization, and stop gradient, etc, and reduces the training epochs by two orders of magnitude. We show that the proposed method is able to converge to 85.1% on CIFAR-10, 58.5% on CIFAR-100, 38.1% on Tiny ImageNet and 58.5% on ImageNet-100 in just one epoch. Furthermore, the proposed method achieves 91.5% on CIFAR-10, 70.1% on CIFAR-100, 51.5% on Tiny ImageNet and 78.9% on ImageNet-100 with linear probing in less than ten training epochs. In addition, we show that EMP-SSL shows significantly better transferability to out-of-domain datasets compared to baseline SSL methods. We will release the code in https://github.com/tsb0601/EMP-SSL.
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition

Named Entity Recognition (NER) is a fundamental NLP tasks with a wide range of practical applications. The performance of state-of-the-art NER methods depends on high quality manually anotated datasets which still do not exist for some languages. In this work we aim to remedy this situation in Slovak by introducing WikiGoldSK, the first sizable human labelled Slovak NER dataset. We benchmark it by evaluating state-of-the-art multilingual Pretrained Language Models and comparing it to the existing silver-standard Slovak NER dataset. We also conduct few-shot experiments and show that training on a sliver-standard dataset yields better results. To enable future work that can be based on Slovak NER, we release the dataset, code, as well as the trained models publicly under permissible licensing terms at https://github.com/NaiveNeuron/WikiGoldSK.
Bipol: A Novel Multi-Axes Bias Evaluation Metric with Explainability for NLP

We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification and sentence-level evaluation based on (sensitive) term frequency (TF). After creating new models to detect bias along multiple axes using SotA architectures, we evaluate two popular NLP datasets (COPA and SQUAD). As additional contribution, we created a large dataset (with almost 2 million labelled samples) for training models in bias detection and make it publicly available. We also make public our codes.
Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning

This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simultaneously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline significantly increases the overall training time. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation, which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem. We prove that our method ensures fast convergence of the model compression problem, and the convergence of the SLR is accelerated by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate our method on image classification tasks using CIFAR-10 and ImageNet with state-of-the-art MLP-Mixer, Swin Transformer, and VGG-16, ResNet-18, ResNet-50 and ResNet-110, MobileNetV2. We also evaluate object detection and segmentation tasks on COCO, KITTI benchmark, and TuSimple lane detection dataset using a variety of models. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves a higher compression rate than state-of-the-art methods under the same accuracy requirement and also can achieve higher accuracy under the same compression rate requirement. Under classification tasks, our SLR approach converges to the desired accuracy $3\times$ faster on both of the datasets. Under object detection and segmentation tasks, SLR also converges $2\times$ faster to the desired accuracy. Further, our SLR achieves high model accuracy even at the hard-pruning stage without retraining, which reduces the traditional three-stage pruning into a two-stage process. Given a limited budget of retraining epochs, our approach quickly recovers the model's accuracy.