Reviewerly/exHarmony huggingface dataset:
The Effects of Spillway Width on Outflow Discharge and Flow Elevation for the Probable Maximum Flood (PMF)

The purpose of this study was to analyze the effect of spillway width on flow elevation at the weir crest based on the flood discharge design for the Probable Maximum Flood (PMF) return period using flood routing hydrologically at the Cacaban Dam (Indonesia). The rainfall Probable Maximum Precipitation (PMP) design uses the Hershfield Equation. The design of the flood discharge analysis of QPMF used the Nakayasu Synthetic Unit Hydrograph (HSS). Flood routing uses the hydrologic routing method. The Cacaban Dam is located in Jati Village, Kedung Banteng District, Tegal Regency, Central Java Province, Indonesia. The results of the research data analysis showed that increased spillway crest widths led to decreased flow evaluation at the spillway crest, and increased outflow discharge. Thus, if a large storage volume of the reservoir is intended, then the width of the spillway crest must be reduced. Otherwise, the width of the spillway crest must be increased. In terms of flood control in the Tegal Regency, it's better to make the crest of the spillway smaller. Doi: 10.28991/CEJ-2022-08-04-08 Full Text: PDF
Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data

In order to reduce the cost of data transmission, the meter data management system (MDMS) of the power operator usually delays time to obtain the power generation information of a solar photovoltaic (PV) power generation system. Although this approach solves the problem of data transmission cost, it brings more challenges to the solar PV power generation forecast. Because power operators usually need real-time solar PV power generation as a basis for the power dispatch, but considering the cost of communication, they cannot always provide corresponding historical power generation data in real time. In this study, an intelligent solar PV power generation forecasting mechanism combined with weather information is designed to cope with the issue of the absence of real-time power generation data. Firstly, the Pearson correlation coefficient analysis is used to find major factors with a high correlation in relation to solar PV power generation to reduce the computational burden of data fitting via a deep neural network (DNN). Then, the data preprocessing, including the standardization and the anti-standardization, is adopted for data-fitting or real-time solar PV power generation data to take as the input data of a long short-term memory neural network (LSTM). The salient features of the proposed DNN-LSTM model are: (1) only the information of present solar PV power generation is required to forecast the one at the next instant, and (2) an on-line learning mechanism is helpful to adjust the trained model to adapt different solar power plant or environmental conditions. In addition, the effectiveness of the trained model is verified by six actual solar power plants in Taiwan, and the superiority of the proposed DNN-LSTM model is compared with other forecasting models. Experimental verifications show that the proposed forecasting model can achieve a high accuracy of over 97%.
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Despite their wide adoption, the underlying training and memorization dynamics of very large language models is not well understood. We empirically study exact memorization in causal and masked language modeling, across model sizes and throughout the training process. We measure the effects of dataset size, learning rate, and model size on memorization, finding that larger language models memorize training data faster across all settings. Surprisingly, we show that larger models can memorize a larger portion of the data before over-fitting and tend to forget less throughout the training process. We also analyze the memorization dynamics of different parts of speech and find that models memorize nouns and numbers first; we hypothesize and provide empirical evidence that nouns and numbers act as a unique identifier for memorizing individual training examples. Together, these findings present another piece of the broader puzzle of trying to understand what actually improves as models get bigger.
Federated Learning in Healthcare: A Privacy Preserving Approach

A need to enhance healthcare sector amidst pandemic arises. Many technological developments in Artificial Intelligence (AI) are being constantly leveraged in different fields of healthcare. One such advancement, Federated Learning(FL) has acquired recognition primarily due to its decentralized, collaborative nature of building AI models. The most significant feature in FL is that, raw data remain with the data sources throughout the training process and thus preventing its exposure. Hence, FL is more suitable and inevitable in healthcare domain as it deals with private sensitive data which needs to be protected. However, privacy threats still exist in FL, necessitating a requirement for further improvement in privacy protection This paper discusses about the concepts and applications of FL in healthcare and presents a novel approach for enhancing privacy preservation in Federated Learning.
Technological pedagogical content knowledge of primary school science teachers during the COVID-19 in Thailand and Finland

Technological pedagogical content knowledge (TPACK) is important for teaching science during the COVID-19 pandemic. This paper investigates the TPACK of Finnish and Thai primary school teachers in the context of teaching science through blended learning (BL) during the COVID-19. 11 teachers from Finland and Thailand were interviewed. The interview data were examined using deductive content analysis. The analysis revealed that all teachers used educational technology in their online classes in terms of providing the lesson content, learning activities, and the students' learning assessment. Zoom and MS Teams were the tools used for online teaching in both countries. The main teaching method used in both types of instruction was experimentation. For online instruction, most teachers considered educational technology in every step of the teaching process to enhance students' learning of science as much as possible. Many types of direct and technology-mediated interaction appeared during BL, especially during online teaching, which could be designed and analyzed in the context of the TPACK model.
influence of digital creative technologies on the development of education and medicine

A concise and factual abstract The purpose of scientific research is to trace the patterns of change in the development of education and medicine and justify the direction in which they will develop so that people will be ready for rapid change, and education prepares professionals capable of implementing new digital technologies that improve human health. The methodology of the study is to use such methods as analysis and synthesis, categorical analysis, comparative analysis, sociological method, and other methods of scientific knowledge. The purpose of the study was to identify patterns of change in the development of medicine and education due to the impact on them of digital technology. The article reveals the content and essence of digital technologies, which are introduced in medicine, require training in the provision of medical care; analyzes the directions of development of medicine in the context of philosophical reflection, and tests the effectiveness of their use. The practical significance of the study lies in the philosophical understanding of the interaction between education and medicine, according to which the future of our civilization, based on cellular medicine, the discovery of stem cells, which are developed using modern technology and new standards of medicine.
Large Language Models are Few-Shot Clinical Information Extractors

A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations. In this work, we show that large language models, such as InstructGPT, perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. Whereas text classification and generation performance have already been studied extensively in such models, here we additionally demonstrate how to leverage them to tackle a diverse set of NLP tasks which require more structured outputs, including span identification, token-level sequence classification, and relation extraction. Further, due to the dearth of available data to evaluate these systems, we introduce new datasets for benchmarking few-shot clinical information extraction based on a manual re-annotation of the CASI dataset for new tasks. On the clinical extraction tasks we studied, the GPT-3 systems significantly outperform existing zero- and few-shot baselines.
Augmented Reality: The Effect in Students’ Achievement, Satisfaction and Interest in Science Education

The purpose of this study was to analyse the students' views and the effects of using Augmented Reality (AR) in learning Science. Given that Science education emphasizes the understanding of the physical and the natural world, the science lesson is basically received through systematic observation and experimentation. The introduction of abstract concepts in the science lesson is implemented gradually by scaffolding the concrete understanding during primary schooling. The potential use of AR, as a teaching tool in facilitating the process of understanding concrete facts, could be beneficial in science education. The introductory topic, "Senses," was chosen; since it is directly related to the students’ anatomy; and it cultivates their interest in Science. A quasi-experimental methodology was utilized to examine the impact of AR on primary school students’ academic achievement, interest, and science-process skills in this study. During the science lesson, the experimental group was exposed to AR stimulation, whereas the control group was not; and rather it learnt through the conventional method. A set of post-test questions was conducted, in order to collect the data on student achievement and science process skills, while a set of questionnaires was employed, in order to identify the students’ interest. The data were analysed by using descriptive statistics and the t-test. In this study, the real world had been augmented by using virtual information, thereby providing new possibilities for science education to become more meaningful. The findings indicated that AR had a significant favourable effect on all three aspects of the experimental group's achievement, interest, and science-process skills.
SwinT-Unet: Hybrid architecture for Medical Image Segmentation Based on Swin transformer block and Dual-Scale Information

The fast development of Convolution Neural Networks (CNN) based on U-shaped architecture has shown innovative improvements in the fields of image segmentation. However, these approaches cannot learn global information in images due to the local aspect of the convolution operation. This paper deals with designing a hybrid method of medical image segmentation. Taking advantage of Shifted windows (Swin) transformer block to extract fine-grained features and the Transformer Interactive Fusion (TIF) module to create a fusion between features of different scales, the proposed approach consists of a dual-Scale encoder&#x2013;Swin transformer U-shaped architecture (SwinT-Unet). The effectiveness of this method has been evaluated on the Synapse multi-organ CT dataset. The suggested segmentation demonstrated more efficiency than the results of some other current methods.
Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.
Using AR filters in L2 pronunciation training: practice, perfection, and willingness to share

Traditional L2 pronunciation teaching puts too much emphasis on explicit phonological knowledge ('knowing that') rather than on procedural knowledge ('knowing how'). The advancement of mobile-assisted language learning (MALL) offers new opportunities for L2 learners to proceduralize their declarative articulatory knowledge into production skills more efficiently through systematic practice. Within the framework of skill acquisition theory, and using a mixed-methods design including production tests, articulatory awareness quizzes, and a semi-structured interview, this study aims to investigate the effectiveness of using an augmented reality (AR) filter app during pronunciation practice to improve Chinese L2 students' segmental production and articulatory awareness of the targeted English consonants (dark/É«/,/ð/and/θ/), and their attitudes towards this application. The experimental results indicate that the app with AR technology, facial detection technology, and digital zoom technology played a positive role in promoting students' segmental production and raising their articulatory awareness. The interview results show that the AR filter helped to alleviate the learners' speaking anxiety and embarrassment related to interacting with the teacher for corrective feedback, and encouraged daily practice by providing more fun. Combining all the qualitative and quantitative results, we conclude that an AR filter app can offer academic, psychological, and social support for L2 pronunciation skill learning.
Performance Evaluation of Cryptographic Algorithms: DES, 3DES, Blowfish, Twofish, and Threefish

With the advancement of the Internet, many individuals and organizations have started to use it to store and send personal or business information. Some of this information is highly confidential and that, in turn, raises an issue of data privacy and confidentiality. Major advances in the Internet have also aided intruders in getting unauthorized access to confidential information. Information Security is the art of securing data from illegitimate access while ensuring integrity, availability, and confidentiality of data. Information Security is achieved through cryptographic encryption and decryption algorithms. Encryption hides confidential information by converting it to an unreadable form while the reverse process of retrieving data from the unreadable or encrypted form is known as decryption. Many cryptographic algorithms exist today and selecting which one to use depends on several factors and measures. This paper conducts a comparison of the encryption speed for five different cryptographic symmetric block-cipher algorithms: DES, 3DES, Blowfish, Twofish, and Threefish. The simulation is done using Python with various text file sizes. The results show that Blowfish outperforms the rest of the algorithms that were tested.
Wearable Choreographer: Designing Soft-Robotics for Dance Practice

In this pictorial, we describe an auto-biographical design process that led to the fabrication of a soft robotic wearable for lower limb movement guidance that we designated Wearable Choreographer. We first explored the design from a first-person perspective and then shared it with four dancers. Our experiments illustrate how the wearable both constrains and inspires the dancers towards new ways of performing, challenging them to rethink their movements. Our design inquiry contributes with reflections on soft robotics that uncover the challenges and prospects designers and researchers in Human-Computer Interaction face when designing, prototyping and experimenting with such technologies for embodied interactions.
Making Large Language Models Better Reasoners with Step-Aware Verifier

Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress in this area, but they still face difficulties in reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve their reasoning skills, previous work has proposed to guide the language model with prompts that elicit a series of reasoning steps before giving the final answer, achieving a significant improvement on GSM8K from 17.9% to 58.1% in problem-solving rate. In this paper, we present DIVERSE (Diverse Verifier on Reasoning Step), a novel approach that further enhances the reasoning capability of language models. DIVERSE has three main components: first, it generates diverse prompts to explore different reasoning paths for the same question; second, it uses a verifier to filter out incorrect answers based on a weighted voting scheme; and third, it verifies each reasoning step individually instead of the whole chain. We evaluate DIVERSE on the latest language model code-davinci-002 and show that it achieves new state-of-the-art results on six of eight reasoning benchmarks (e.g., GSM8K 74.4% to 83.2%).
The Effect of Transactional Leadership on Innovative Work Behavior: Testing the Role of Knowledge Sharing and Work Engagement as Mediation Variables

This study, in detail, aims to explore and examine the effect of (1) transactional leadership on knowledge sharing, work engagement, and innovative work behavior; (2) knowledge sharing on innovative work behavior; (3) work engagement on knowledge sharing and innovative work behavior; and (4) knowledge sharing and work engagement in mediating the relationship between transactional leadership and innovative work behavior.The population in this study includes line managers, supervisors, and functional staff working in four stone milling companies in Central Java, Indonesia.This research is quantitative in nature, where the research data that has been collected will be processed and analyzed using structural equation modeling (SEM) based on SmartPLS 3.0 version.By analyzing 107 respondents, the results of this study conclude that (1) transactional leadership has a significant effect on knowledge sharing and work engagement, but not on innovative work behavior; (2) work engagement not only has a significant effect on knowledge sharing but also on innovative work behavior; (3) knowledge sharing has a significant effect on innovative work behavior; and (4) knowledge sharing and work engagement fully mediates the relationship between transactional leadership and innovative work behavior.This study gives a comprehensive understanding that knowledge sharing and work engagement become essential variables in linking transactional leadership and innovative work behavior.
Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure

The automatic registration of multisensor remote sensing images is a highly challenging task due to the inherently different physical, statistical, and textural characteristics of the input data. Information-theoretic measures are often used to favor comparing local intensity distributions in the images. In this paper, a novel method based on the combination of a deep learning architecture and a correlation-type area-based functional is proposed for the registration of a multisensor pair of images, including an optical image and a synthetic aperture radar (SAR) image. The method makes use of a conditional generative adversarial network (cGAN) in order to address image-to-image translation across the optical and SAR data sources. Then, once the optical and SAR data are brought to a common domain, an area-based ℓ2 similarity measure is used together with the COBYLA constrained maximization algorithm for registration purposes. While correlation-type functionals are usually ineffective in the application to multisensor registration, exploiting the image-to-image translation capabilities of cGAN architectures allows moving the complexity of the comparison to the domain adaptation step, thus enabling the use of a simple ℓ2 similarity measure, favoring high computational efficiency, and opening the possibility to process a large amount of data at runtime. Experiments with multispectral and panchromatic optical data combined with SAR images suggest the effectiveness of this strategy and the capability of the proposed method to achieve more accurate registration as compared to state-of-the-art approaches.
Explainable detection of adverse drug reaction with imbalanced data distribution

Analysis of health-related texts can be used to detect adverse drug reactions (ADR). The greatest challenge for ADR detection lies in imbalanced data distributions where words related to ADR symptoms are often minority classes. As a result, trained models tend to converge to a point that strongly biases towards the majority class and then ignores the minority class. Since the most used cross-entropy criteria is an approximation to accuracy, the model focuses more readily on the majority class to achieve high accuracy. To address this issue, existing methods apply either oversampling or down-sampling strategies to balance the data distribution and exploit the most difficult samples of the minority class. However, increasing or reducing the number of individual tokens alone in sequence labeling tasks will result in the loss of the syntactic relations of the sentence. This paper proposes a weighted variant of conditional random field (CRF) for data-imbalanced sequence labeling tasks. Such a weighting strategy can alleviate data distribution imbalances between majority and minority classes. Instead of using softmax in the output layer, the CRF can capture the relationship of labels between tokens. The locally interpretable model-agnostic explanations (LIME) algorithm was applied to investigate performance differences between models with and without the weighted loss function. Experimental results on two different ADR tasks show that the proposed model outperforms previously proposed sequence labeling methods.