Reviewerly/exHarmony huggingface dataset:
Using electronic health records to monitor, augment and evaluate patient care in Singapore

Abstract The advent of electronic health records (eHRs) has opened up many opportunities for researchers and practitioners in healthcare to improve human lives. The opportunity to monitor and evaluate events or processes in real time on a large scale is feasible with big data, thereby facilitating timely information and the practice of proactive care. Predictive analytics allows the re-engineering of processes by augmenting them with predictive scores to identify individuals at high risk of adverse events for early intervention. However, to seize these opportunities to improve patient care, alleviating the challenges faced when utilizing eHRs to generate actionable insights is important to unleash the full potential of eHRs. With better access to eHRs and relevant domain experts, it facilitates the appropriate use of eHRs and analytical approaches. To illustrate some of these opportunities and challenges, the following two case studies will be discussed during this presentation: (i) programs developed at an academic health system in Singapore that aim to improve the experience and outcomes of inpatients, and (ii) a nationwide Predictive Model for Admission Prevention in Singapore that identifies suitable discharged inpatients for enrolment into a community-centric program.
Symbolic Techniques for Deep Learning: Challenges and Opportunities

As the number of deep learning frameworks increase and certain ones gain popularity, it spurs the discussion of what methodologies are employed by these frameworks and the reasoning behind them. The goal of this survey is to study how symbolic techniques are utilized in deep learning. To do this, we look at some of the most popular deep learning frameworks being used today, including TensorFlow, Keras, PyTorch, and MXNet. While these frameworks greatly differ from one another, many of them use symbolic techniques, whether it be symbolic execution, graphs, or programming. We focus this paper on symbolic techniques because they influence not only how neural networks are built but also the way in which they are executed. Limitations of symbolic techniques have led to efforts in integrating symbolic and nonsymbolic aspects in deep learning, opening up new possibilities for symbolic techniques. For example, the Gluon API by Apache MXNet bridges the gap between imperative programming and symbolic execution through hybridization. Frameworks such as JANUS attempt to translate imperative programs into symbolic graphs, while approaches like DeepCheck attempt to use symbolic execution to analyze and validate imperative neural network programs. Symbolic analysis has also been paired with concrete execution in a technique called concolic testing in order to better test deep neural networks. Our study of these developments exemplifies just a few of the many ways the symbolic techniques employed by popular frameworks have the opportunity to be altered and utilized to achieve better performance.
Inner Ensemble Networks: Average Ensemble as an Effective Regularizer

We introduce Inner Ensemble Networks (IENs) which reduce the variance within the neural network itself without an increase in the model complexity. IENs utilize ensemble parameters during the training phase to reduce the network variance. While in the testing phase, these parameters are removed without a change in the enhanced performance. IENs reduce the variance of an ordinary deep model by a factor of $1/m^{L-1}$, where $m$ is the number of inner ensembles and $L$ is the depth of the model. Also, we show empirically and theoretically that IENs lead to a greater variance reduction in comparison with other similar approaches such as dropout and maxout. Our results show a decrease of error rates between 1.7\% and 17.3\% in comparison with an ordinary deep model. We also show that IEN was preferred by Neural Architecture Search (NAS) methods over prior approaches. Code is available at https://github.com/abduallahmohamed/inner_ensemble_nets.
Evidence On The Links Between Compensation, Job Satisfaction And Task Performance

Nurses play a vital role in the provision of healthcare services. They represent the bulk of the healthcare workforce and act as frontliners in serving patients particularly during the present Covid19 pandemic. Their performance at work is closely linked to the productivity and quality of care delivered by the healthcare organization. Compensation has a strategic role in fulfilling employees’ needs and desires. Compensation has been widely acknowledged as a precursor to job satisfaction and greatly impact employees' job performance. Therefore, this study aims to examine the links between compensation, job satisfaction, and task performance of nurses. Self-administered questionnaires were used to collect the survey data on 354 staff nurses working in public hospitals in Malaysia. Data was subsequently analysed using Partial Least Squares (PLS) method. Our results revealed that compensation has a positive relationship with job satisfaction. Likewise, job satisfaction has a positive relationship with task performance. Furthermore, satisfaction was found to significantly mediate the relationship between compensation and task performance. The present findings suggest that policy makers need to develop an attractive and fair compensation package so that nurses become satisfied with their jobs, which in turn, trigger them to exhibit superior nursing performance. Finally, useful insights to healthcare authorities are provided to further improve the contribution and reputation of the overall healthcare industry.
Multikey Image Encryption Algorithm Based on a High-Complexity Hyperchaotic System

In life, a chaotic system has many applications in different fields, including physics, biology, communication, and cryptography. In this study, a new hyperchaotic system is introduced. This hyperchaotic system is a two-dimensional system that is based on three maps-namely, logistic, iterative chaotic, and Henon maps. The dynamics of this system are investigated using maximal Lyapunov exponents, bifurcation diagrams, phase portraits, basin of attraction, and complexity via entropy. This system shows highly complicated dynamics. On the basis of the proposed system, a new algorithm for image encryption is also introduced. Confusion and diffusion can be achieved with this algorithm, which are fundamental demands. The stochastic behavior of this system is used to reinforce the security of the encrypted image. The image is divided into four parts, each of which uses a different random key established by the proposed chaotic system. The security of this cryptosystem is validated on the basis of key security parameters and common attacks.
Peningkatan Kompetensi Keilmuan IOT Melalui Pelatihan Pengontrolan Perangkat IOT dengan Menggunakan Smartphone untuk Siswa SMK dan SMA di Kota Malang

The internet has encouraged the emergence of a new paradigm in the use of computing, marked by the emergence of devices that are connected to each other or known as the Internet of Things (IoT). One of the factors that contributed to the rapid emergence of "things" in the IoT network was the rapid development of chip technology and also increasingly varied wireless communication technology. With the emergence of the phenomenon of the development of IoT, especially those involving entrepreneurial opportunities in related fields, it is considered important to be introduced early on to students of vocational-high school equivalents in Indonesia. The activity was held in September 2019 at Filkom UB involving 41 participants from 6 schools of vocational-high school or equivalent. The activity succeeded in increasing the participant's understanding of the theory and also the practice of IoT where the participants' pre-test and post-test scores increased from an average of 61.6 to 92.9. The implementation of the activity was also declared successful from the results of the questionnaire. From a total of 425 answers, there were 17 STS answers, 39 TS answers, 205 S answers and 164 SS answers. Taking into account the positive and negative components, a recapitulation of 415 answers (98%) was found to be satisfied with the implementation of the activities.
Use of office package of free software in high school of Santa Rosalía, BCS

The present article shows the results of the study carried to the junior high schools of Santa Rosalía Baja California Sur, to identify the level of knowledge that the students and teachers have about the free office software package, where the result obtained was a high percentage of ignorance. It should be noted the great availability and acceptance shown by students and teachers towards this software package based on free software. To carry out this study, it was used the exploratory and descriptive methods, where the study sample was a total of 135 students and 44 teachers who replied the surveys and interviews, which were conducted in two stages: the first one, was carried out to know the level of knowledge the school community has about of office programs of free software; the second one, was done to know the level of acceptance of it.
Information Hiding Application Using Digital Images Secured With Rijndael and Caesar Cipher Encryption

Steganography is a way thtt conceals or embeds important data in form of text file into an image which possible to reduce and erase the existence of the data. A modern steganography can help millions of people to protect their data from potential threats or other individuals using computer programs and algorithm. This paper proposed an implementation of steganography Rijndael and Caesar schemes . This application will let the user embed the important data/information to an image and encrypt it.
Perceived Environments Impact on Exercise Using Virtual Reality

By Andrew Miller, Health Sciences; Michael Bacigalupo, Health Sciences; Abbey Hammann, Health Sciences; Matthew Horsley, University of Cincinnati; Kendall Lyman, University of Cincinnati; Robbie Lynch, University of Cincinnati; Maya Pentecost, University of Cincinnati; Aaron Shepherd, University of Cincinnati; Alaina Tarr, University of Cincinnati Advisor: Susan Kotowski Abstract: The usage of virtual reality (VR) in exercise has been known to have an overall positive impact. Virtual reality can improve an individual's endurance, performance, and motivation in addition to decreasing RPE (rating of perceived exertion). This study investigated the effects of perceived environment on exercise performance using VR. The exercise movement utilized in these trials was bicep curls. The subject pool was a sample of convenience with an average age of 20.5 years in males (n=4) and 22 years in females (n=6). Males had a standard curling weight of 15 lbs. and females of 10 lbs. Subjects completed trials with both their non-dominant and dominant arms in each environment. The environments included a baseline (no VR), serene scenes from National Geographic Lions documentary, and stressful or intense scenes from the movie Captain America: Civil War. Recorded variables included heart rate, RPE, time, number of repetitions, and motivation level. In addition to quantitative data, qualitative data was also collected. A significant difference (p < 0.05) was found when comparing the baseline versus action scene and the action versus relaxing scene. Likewise, motivation was significantly higher during the action spouts when comparing baseline or relaxing scenes. Overall, action scenes resulted in a significant increase in the repetitions completed in both the dominant and non-dominant arms, while the relaxing scenes did not necessarily cause a significant difference. Use of VR may be dependent on the specific scenes involved and further research should be completed to test this hypothesis.
VisualNews : A Large Multi-source News Image Dataset

In this paper we propose VisualNews-Captioner, an entity-aware model for the task of news image captioning. We also introduce VisualNews, a large-scale benchmark consisting of more than one million news images along with associated news articles, image captions, author information, and other metadata. Unlike the standard image captioning task, news images depict situations where people, locations, and events are of paramount importance. Our proposed method is able to effectively combine visual and textual features to generate captions with richer information such as events and entities. More specifically, we propose an Entity-Aware module along with an Entity-Guide attention layer to encourage more accurate predictions for named entities. Our method achieves state-of-the-art results on both the GoodNews and VisualNews datasets while having significantly fewer parameters than competing methods. Our larger and more diverse VisualNews dataset further highlights the remaining challenges in captioning news images.
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of (connected) triples. Two main challenges still face the current crop of encoder-decoder-based methods, especially on small subgraphs: (1) low diversity and poor fluency due to the limited information contained in the subgraphs, and (2) semantic drift due to the decoder's oblivion of the semantics of the answer entity. We propose an innovative knowledge-enriched, type-constrained and grammar-guided KBQG model, named KTG, to addresses the above challenges. In our model, the encoder is equipped with auxiliary information from the KB, and the decoder is constrained with word types during QG. Specifically, entity domain and description, as well as relation hierarchy information are considered to construct question contexts, while a conditional copy mechanism is incorporated to modulate question semantics according to current word types. Besides, a novel reward function featuring grammatical similarity is designed to improve both generative richness and syntactic correctness via reinforcement learning. Extensive experiments show that our proposed model outperforms existing methods by a significant margin on two widely-used benchmark datasets SimpleQuestion and PathQuestion.
Attaining Real-Time Super-Resolution for Microscopic Images Using GAN

In the last few years, several deep learning models, especially Generative Adversarial Networks have received a lot of attention for the task of Single Image Super-Resolution (SISR). These methods focus on building an end-to-end framework, which produce a high resolution(SR) image from a given low resolution(LR) image in a single step to achieve state-of-the-art performance. This paper focuses on improving an existing deep-learning based method to perform Super-Resolution Microscopy in real-time using a standard GPU. For this, we first propose a tiling strategy, which takes advantage of parallelism provided by a GPU to speed up the network training process. Further, we suggest simple changes to the architecture of the generator and the discriminator of SRGAN. Subsequently, We compare the quality and the running time for the outputs produced by our model, opening its applications in different areas like low-end benchtop and even mobile microscopy. Finally, we explore the possibility of the trained network to produce High-Resolution HR outputs for different domains.
Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?

Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures. One such model is ON-LSTM (Shen et al., 2019), which is trained on language modelling and has near-state-of-the-art performance on unsupervised parsing. In order to better understand the performance and consistency of the model as well as how the parses it generates are different from gold-standard PTB parses, we replicate the model with different restarts and examine their parses. We find that (1) the model has reasonably consistent parsing behaviors across different restarts, (2) the model struggles with the internal structures of complex noun phrases, (3) the model has a tendency to overestimate the height of the split points right before verbs. We speculate that both problems could potentially be solved by adopting a different training task other than unidirectional language modelling.
Historical Features of the Development of the Contents of the Subject «Self-cognition»

In the article, the authors proposed periodization of the development of the subject of «Self-cognition» in the school education of Kazakhstan, which was developed on the basis of the analysis of conceptual and methodological approaches, the methodological system of the subject «Self-cognition» itself. The structural elements of the content of the moral and spiritual education of «Self-cognition» of the updated version are considered. The features of the updated content of the subject «Self-cognition» in the elementary school, which contribute to the formation of humanistic qualities of the individual and lead to the spiritual growth of a person, are revealed. Periodization includes the stages: preparatory and experimental stages (until 2010); implementation (2010-2015), the update phase (from 2015). The experience of teaching the subject «Self-cognition» is systematized and generalized, a comparison of the development of stages (experimental → introduction → update stage) is carried out according to the criteria: continuity of normative documents, a brief description of the conceptual foundations of changes, leading ideas for constructing content, changing the didactic design of the lesson, changing teaching methods. The analysis showed that the leading idea, the concept of the subject and its implementation in Kazakhstan are strategically and prognostically relevant and significant at the present stage of developmentof society. It can be called as a response of Kazakhstani education to modern crisis phenomena in a globalizing world. The modern methodological tools used to study self-cognition are innovative and advanced, both in terms of leading didactic and methodological scientific approaches (synergetic, axiological, value-semantic, environmental, design and other approaches), and in modern pedagogical tools (storytelling, creative work and art methods, singing, etc.). That is, the built-up strategies, methods and teaching technologies, at the moment, meet the global development of the science of education.Key words: spiritual and moral education, self-conition, universal values, updated content, project activities.
Topic Diffusion Discovery Based on Deep Non-negative Autoencoder

Researchers have been overwhelmed by the explosion of research articles published by various research communities. Many research scholarly websites, search engines, and digital libraries have been created to help researchers identify potential research topics and keep up with recent progress on research of interests. However, it is still difficult for researchers to keep track of the research topic diffusion and evolution without spending a large amount of time reviewing numerous relevant and irrelevant articles. In this paper, we consider a novel topic diffusion discovery technique. Specifically, we propose using a Deep Non-negative Autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change with time. The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.
Model Exploration with Cost-Aware Learning

We present an extension to active learning routines in which non-constant costs are explicitly considered. This work considers both known and unknown costs and introduces the term ε-frugal for learners that do not only consider minimizing total costs but are also able to explore high cost regions of the sample space. We demonstrate our extension on a well-known machine learning dataset and find that out ε-frugal learners outperform both learners with known costs and random sampling.
Visual Analysis of the Research and Application of Learning Resource Recommendation System

With the development of information technology, the learning resource recommendation system as a means of auxiliary teaching has gradually become the research focus of many scholars in the field of education. In this paper, CiteSpace, a visualization software, is used to make a quantitative statistical analysis of journal literature related to the learning resource recommendation system in Web of Science, and four research topics are obtained: recommendation system, collaborative filtering, ontology, and E-learning. This paper summarizes the existing problems and development trends of the study of the learning resource recommendation system.
A new fully homomorphic cryptosystem based on a super-increasing sequence

Cloud computing is a powerful computing paradigm that provides a variety of computing services to its users. An example is storage, which allows individuals and enterprises to outsource their files to remote storage. However, saving private information onto third-party storage increases the security issues of data and privacy protection concerns. For this reason, cloud service providers (CSPs) are required to save an encrypted version of user data. In this paper, a novel encryption technique based on the use of Fully Homomorphic Encryption is presented. The technique uses a superincreasing sequence to derive the key and works on encrypted data with no need for decryption; this yields the same results as performing it on plaintext data. In the proposed technique, the characters are converted to their corresponding ASCII code values, which differs from the binary values produced by other existing techniques.
MEDIA PROMOSI MENGGUNAKAN VIDEO IKLAN BERBASIS ADOBE PREMIERE PRO DAN ADOBE AFTER EFFECT DI TOUR UMROH DAN HAJI MABRURO KOTA GRESIK

The purpose of this research was to design video advertisement with using Adobe Premiere and Adobe After Effect as support to promotional media in Tour Umroh Dan Haji Mabruro Gresik. Type of this research was Action Research. Video advertisement, was created by using Adobe Premiere and Adobe After Effect application. Data collection method was observation, interview, and questionnaires. The unit analysis was from the experts and the owners. Data analysis was using EPIC rate as measurement scale.  In this research, there were 2 cycles. Cycle 1 showed that video advertisement was still not good and need to continue to the next cycle 2. Cycle 2 was repaired based on advice from the computer and marketing experts. Then finally the advertisement video was worth it to published. After published, it obtained value .1 from measurement scale of EPIC rate. Based on this research can be concluded that the video advertisement of  Tour Umroh dan Haji Mabruro was effective, based on measurement scale of EPIC rate.