Reviewerly/exHarmony huggingface dataset:
Knowledge translation of prediction rules: methods to help health professionals understand their trade-offs

Clinical prediction models are developed with the ultimate aim of improving patient outcomes, and are often turned into prediction rules (e.g. classifying people as low/high risk using cut-points of predicted risk) at some point during the development stage. Prediction rules often have reasonable ability to either rule-in or rule-out disease (or another event), but rarely both. When a prediction model is intended to be used as a prediction rule, conveying its performance using the C-statistic, the most commonly reported model performance measure, does not provide information on the magnitude of the trade-offs. Yet, it is important that these trade-offs are clear, for example, to health professionals who might implement the prediction rule. This can be viewed as a form of knowledge translation. When communicating information on trade-offs to patients and the public there is a large body of evidence that indicates natural frequencies are most easily understood, and one particularly well-received way of depicting the natural frequency information is to use population diagrams. There is also evidence that health professionals benefit from information presented in this way.Here we illustrate how the implications of the trade-offs associated with prediction rules can be more readily appreciated when using natural frequencies. We recommend that the reporting of the performance of prediction rules should (1) present information using natural frequencies across a range of cut-points to inform the choice of plausible cut-points and (2) when the prediction rule is recommended for clinical use at a particular cut-point the implications of the trade-offs are communicated using population diagrams. Using two existing prediction rules, we illustrate how these methods offer a means of effectively and transparently communicating essential information about trade-offs associated with prediction rules.
Analisis Sentimen Berbasis Emoticon pada Komentar Instagram Bahasa Indonesia Menggunakan Naïve Bayes

The usage of social media growing rapidly, especially after the smartphone was invented. Because the number of social media users was quite a lot, companies prefer to promote their products through social media like Instagram. But, unlike TV or radio, social media is a two-way communication media, that makes users can respond directly to the content created by the company. Comments given by users have various types of sentiment, like positive or negative comments. In addition to using text, comments also often contain emoticons to support the message. This study tries to analyzing sentiment based on the usage of emoticons inside them using the Naïve Bayes algorithm. Based on the test results, the accuracy result is quite good, it is about 96.3% correct in sentiment classification.
A Framework for Feature Selection Using Natural Language Processing for User Profile Learning for Recommendations of Healthcare-Related Content

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.
Exception Handling

In this chapter, you will learn: error handling in Java using exceptions, the different types of exceptions, using try-catch blocks, and using auto-closeable resources in a try-catch block.
GTF: An Adaptive Network Anomaly Detection Method at the Network Edge

Network Anomaly Detection (NAD) has become the foundation for network management and security due to the rapid development and adoption of edge computing technologies. There are two main characteristics of NAD tasks: tabular input data and imbalanced classes. Tabular input data format means NAD tasks take both sparse categorical features and dense numerical features as input. In order to achieve good performance, the detection model needs to handle both types of features efficiently. Among all widely used models, Gradient Boosting Decision Tree (GBDT) and Neural Network (NN) are the two most popular ones. However, each method has its limitation: GBDT is inefficient when dealing with sparse categorical features, while NN cannot yield satisfactory performance for dense numerical features. Imbalanced classes may downgrade the classifier’s performance and cause biased results towards the majority classes, often neglected by many exiting NAD studies. Most of the existing solutions addressing imbalance suffer from poor performance, high computational consumption, or loss of vital information under such a scenario. In this paper, we propose an adaptive ensemble-based method, named GTF, which combines TabTransformer and GBDT to leverage categorical and numerical features effectively and introduces Focal Loss to mitigate the imbalance classification. Our comprehensive experiments on two public datasets demonstrate that GTF can outperform other well-known methods in both multiclass and binary cases. Our implementation also shows that GTF has limited complexity, making it be a good candidate for deployment at the network edge.
One Website to Rule Them All: Lessons Learned from a Series of Reorganizations, Integrations, and Creation of One University Libraries’ Website From Three

Mergers of health sciences libraries into the larger university library system often result in a cascade of changes, including the integration of the health sciences into a unified, system-wide website. This paper will discuss two health sciences libraries’ experiences with reorganizing content and rethinking services, explaining health sciences users’ needs to colleagues in the university library system, and advocating for changes that made sense for a diverse range of users. Alongside considerable challenges, there were positive outcomes from the merger, including increased website usability and strengthened partnerships across the library system, particularly with library IT.
The Role of Event-Based Representations and Reasoning in Language

This chapter reviews the research conducted on the representation of events, from the perspectives of natural language processing, artificial intelligence (AI), and linguistics. AI approaches to modeling change have traditionally focused on situations and state descriptions. Linguistic approaches start with the description of the propositional content of sentences (or natural language expressions generally). As a result, the focus in the two fields has been on different problems. I argue that these approaches have common elements that can be drawn on to view event semantics from a unifying perspective, where we can distinguish between the surface events denoted by verbal predicates and what I refer to as the latent event structure of a sentence. By clearly distinguishing between surface and latent event structures of sentences and texts, we move closer to a general computational theory of event structure, one permitting a common vocabulary for events and the relations between them, while enabling reasoning at multiple levels of interpretation.
Performance of passive decoy-state quantum key distribution with mismatched local detectors

Abstract In quantum key distribution (QKD), the passive decoy-state method can simplify the intensity modulation and reduce some of side-channel information leakage and modulation errors. It is usually implemented with a heralded single-photon source. In Wang et al 2016 ( Phys. Rev. A 96 032312), a novel passive decoy-state method is proposed by Wang et al , which uses two local detectors to generate more detection events for tightly estimating channel parameters. However, in the original scheme, the two local detectors are assumed to be identical, including the same detection efficiency and dark count rate, which is often not satisfied in the realistic experiment. In this paper, we construct a model for this passive decoy-state QKD scheme with two mismatched detectors and explore the effect on QKD performance with certain parameters. We also take the finite-size effect into consideration, showing the performance with statistical fluctuations. The results show that the efficiencies of local detectors affect the key rate more obviously than dark count rates.
Topic-Feature Lattices Construction and Visualization for Dynamic Topic Number

Abstract The topic recognition for dynamic topic number can realize the dynamic update of super parameters, and obtain the probability distribution of dynamic topics in time dimension, which helps to clear the understanding and tracking of convection text data. However, the current topic recognition model tends to be based on a fixed number of topics K and lacks multi-granularity analysis of subject knowledge. Therefore, it is impossible to deeply perceive the dynamic change of the topic in the time series. By introducing a novel approach on the basis of Infinite Latent Dirichlet allocation model, a topic feature lattice under the dynamic topic number is constructed. In the model, documents, topics and vocabularies are jointly modeled to generate two probability distribution matrices: Documents-topics and topic-feature words. Afterwards, the association intensity is computed between the topic and its feature vocabulary to establish the topic formal context matrix. Finally, the topic feature is induced according to the formal concept analysis (FCA) theory. The topic feature lattice under dynamic topic number (TFL_DTN) model is validated on the real dataset by comparing with the mainstream methods. Experiments show that this model is more in line with actual needs, and achieves better results in semi-automatic modeling of topic visualization analysis.
STUDYING THE PROCESSES OF MEDIA PEDAGOGY DEVELOPMENT IN SECONDARY SCHOOLS OF KAZAKHSTAN

The article presents the results of a sociological study of the development of media education in secondary schools of the Republic of Kazakhstan. The authors note that in modern conditions, media literacy plays an essential role in the education system. Such important objects of media pedagogy research as: clip thinking of schoolchildren, the influence of gadgets on the learning process, media socialization, cybersecurity, distorted reality, manipulation of public consciousness become topical issues. These processes have an impact on the younger generation and their parents, and, therefore, requires study. The study was conducted as part of a sociological survey of subjects of the educational process – students, their parents and teachers, with a sample of 16,357 respondents, including students – 4,860, parents – 7,919, teachers – 3,578 people. The study of the processes of media pedagogy development has shown that there is a need to ensure not only the success of students ' socialization, but also the development of media literacy, ensuring cybersecurity, the use of psychological and pedagogical support for students, as well as the use of digital resources of the educational environment for self-realization and further professional self-determination. The development of media pedagogy ensures the successful social development of the individual in modern conditions.
Watermarking berbasis Redundant Discrete Wavelet Transform dan Arnold Transform pada Citra Medis

In telemedicine, data transmission in digital medical images and electronic medical records through the internet is vulnerable to various threats of theft and manipulation. Image watermarking is needed to provide authentication and security to medical images. This paper proposes an image watermarking scheme based on Redundant Discrete Wavelet Transform (RDWT) and Discrete Cosine Transform (DCT) with watermark encryption using Arnold transform. First, the original host medical image was decomposed into four subbands using RDWT. Then, DCT is applied to the LH subband of the RDWT. On the other hand, the watermark is scrambled using Arnold transform to ensure identity security. The singular value of the watermarked image is obtained by modifying the singular value of the host image and the watermark. Tests were carried out on different medical images, namely X-ray, MRI, CT, and ultrasound, with a watermark in a proprietary logo. The host medical image is the same size as the watermark image. The result of this study can provide high authentication, imperceptibility and security in medical images, with an average PSNR value of 65.67 dB, SSIM 1, BER 0, NC 1. This scheme is resistant to JPEG compression, noise addition, filtering, image sharpening, image enhancement, geometric operations, motion blur, image sharpening, and histogram equalization.
Call for papers, JOVACET 5(1), 2022

You are invited to submit an article for the fifth volume of JOVACET to be published in October/November 2022. JOVACET is an accredited publication with the Department of Higher Education and Training.&#x0D; Articles should be topical with regard to current debates/discourses and recent research in the TVET, adult, and continuing education and training domains. Submissions should be of high quality and follow academic research/writing conventions of journal articles in the social sciences. Specifications can be found on the JOVACET website (www.jovacet.ac.za) or obtained from Dr Catherine Robertson at cathy@tcrobertson.co.za.&#x0D; Articles should comprise a maximum of 8000 words, which include the abstract of approximately 200 words and a list of references, and be submitted in MS Word format via the journal website at www.jovacet.ac.za or emailed to Dr Catherine Robertson at cathy@tcrobertson.co.za. Kindly follow the style guide which is provided on the website.&#x0D; We look forward to receiving your submissions!&#x0D; Due date for full paper submissions: 15 April 2022
TWO-DIMENSIONAL COLOUR APPEARANCE SCALES FOR COLOUR STIMULI HAVING HIGH DYNAMIC RANGE

New colour appearance scales close to daily experience and image quality enhancement are highly desired including whiteness, blackness, vividness and depth. This article describes a new experiment to accumulate the data under HDR (high dynamic range) conditions. The data were then used to test the performance of different colour appearance scales such as CIELAB and CAM16-UCS plus the recent extension by Berns’ Vab*, Dab*. The results showed those Berns’ scales gave reasonable performance. However, there was no scale capable of predicting colour appearance data covering a wide dynamic range. New scales were developed based on the absolute scales of brightness and colourfulness of CAM16-UCS and gave accurate prediction to the data.
The Importance Of The Jidoka Autonomation System On Cost Rationalization And Avoiding Wastes (Muda) Applied Research At Ur State Company For Electrical Industries

The research aims to apply modern technologies, including the smart Autonomation system Jidoka (Autonomation Jidoka) because of its effective role in the industrial work environment, including the Ur General Company for Electrical Industries, by applying its modern concepts, and to keep pace with the conditions and developments in the contemporary competitive business environment. Hence, the research problem arose out of not applying these systems, including the smart automation system, Autonomation Jidoka, which negatively affects the cost structure, increased waste, loss, misuse of available resources and customer dissatisfaction. The most important conclusions reached by the research were the failure to keep pace with the technological and economic changes by the company that took place in the work environment, and not to follow many systems, approaches, concepts, philosophies and methods to fit and fit with this environment, which is characterized by economic, administrative and technological variables of modern concepts and methods, including the Jidoka system, The research also reached many recommendations, the most important of which is that the Iraqi industry environment, including the company, which needs to adopt the application of these methods, principles, methods and modern concepts, including the smart automation system, which is a holistic approach, which included all other modern concepts related to the topic of the research referred to.
MFNet algorithm based on indoor scene segmentation

With the advancement of computer performance, deep learning is playing a vital role on hardware platforms. Indoor scene segmentation is a challenging deep learning task because indoor objects tend to obscure each other, and the dense layout increases the difficulty of segmentation. Still, current networks pursue accuracy improvement, sacrifice speed, and augment memory resource usage. To solve this problem, achieve a compromise between accuracy, speed, and model size. This paper proposes Multichannel Fusion Network (MFNet) for indoor scene segmentation, which mainly consists of Dense Residual Module(DRM) and Multi-scale Feature Extraction Module(MFEM). MFEM uses depthwise separable convolution to cut the number of parameters, matches different sizes of convolution kernels and dilation rates to achieve optimal receptive field; DRM fuses feature maps at several levels of resolution to optimize segmentation details. Experimental results on the NYU V2 dataset show that the proposed method achieves very competitive results compared with other advanced algorithms, with a segmentation speed of 38.47 fps, nearly twice that of Deeplab v3+, but only 1/5 of the number of parameters of Deeplab v3 + . Its segmentation results were close to those of advanced segmentation networks, making it beneficial for the real-time processing of images.
A study of phishing website detection based on machine learning methods

Phishing attack is the simplest way to obtain sensitive information from innocent users. The target of phishers is to obtain key information, such as username, password, and bank account details. Cyber security officials are now looking for reliable and stable detection techniques to detect phishing sites. This paper studies the phishing websites detection technology by extracting and analyzing the characteristics of legal and forged Uniform Resource Locators (URLs) using machine learning approaches, including Logistic Regression, K-nearest Neighbor (KNN), Linear Support Vector Classifier (SVC), Random Forest, Gradient Boosting Decision Tree, and Ada-Boost, and compares their performance with respect to criterions such as accuracy, Root Mean Square Error (RMSE), precision, recall, and F1-score. The results show that ensemble methods, including Gradient Boosting Decision Tree, Random Forest, and Ada Boosting, can achieve much higher detection performance than the other algorithms in terms of all the criteria.
GYM REP TRACKER USING MEDIAPIPE AND PYTHON

Human Pose estimation has grabbed the eye of the computer vision community for the past few decades. It is a vital step closer to knowledge people in pics and motion pictures. Strong articulations, small and hardly visible joints, occlusions, apparel, and lighting changes make it very difficult to perform estimate pose. Human Pose estimation is an important problem that needed to be study. It is used to detect human anatomical key points (e.g., shoulder, elbows, legs, wrist, etc.) in real time using less computational resources. There are many Artificial Intelligence models i.e, Posenet, OpenPose1 and MediaPipe8 for Real time Human Pose Estimation. Many experiments has performed to find out the best suitable model for Human Pose Estimation. Experiments stated that PoseNet is suitable to run on lightweight devices like browsers whereas OpenPose meant to run on GPU powered devices and is more accurate. On the other hand, MediaPipe is very fast, modular, reusable and highly efficient. Hence, our model uses the MediaPipe to perform its estimation. Keywords: Pose estimation, Gym Rep Tracker, Media Pipe, Python, Machine learning
New Physics: Sae Mulli, the Past 60 Years and the Next 60 Years

The first issue of the journal “New Physics: Sae Mulli”, one of the representative journals of the Korean Physical Society, was published in May 1961. Since then, Sae Mulli has evolved as an important platform for sharing new findings with those members belonging to the Korean Physical Society. This article presents a brief review of the history of this journal. It also includes the current status of and prospects for this journal.
Investigation of the Necessity of Past Input/output Information in Reinforcement Learning based Robust Control

Reinforcement learning yields a feedback controller that achieves specific control goal (which is often translated as a reward function). However, it often suffers from the Sim2Real gap, and domain randomization is known to be a method to overcome this issue. In this paper, we demonstrate necessity of input/outpu history when domain randomization is employed by a formal example and a simulation result. This is equivalent to the necessity of dynamic feedback controller in terms of control theory.
COVID 19 Data Clustering a nd Testing with K Means Mapper and Reducer

Due to the emergence of a new infectious disease (COVID-19), the worldwide data volume has been quickly increasing at a very high rate during the last two years. Due its infectious, and importance, in this paper, K-Means clustering procedure is applied on COVID data in MapReduce based distributed computing environment. The proposed system is store, process and tests the large volume of COVID-19 data. Experimental results had been proved that this process is adaptable to COVID-19 data in the formation of trusted clusters.
5G application research: integration and future perspectives of blockchain, IoT, and beyond

The fifth-generation (5G) broadband cellular standard, Blockchain and Internet of Things (IoT) are emerging technologies in the present, and they all have many advantages that benefit humanity in a vast variety of technological and scientific fields. The biggest advantages of 5G are higher speeds, low latency and the ability to support up to a million devices per square kilometer. Blockchain is a chain of records that holds data, cryptographic hash and Prove of Work (PoW) using cryptography. IoT is an overall description of devices that are connected through the internet. 5G has the potential to work together with Blockchain technology over its popularization, helping Blockchain with its problems and introducing Blockchain to IoT for more commercial use, while blockchain can benefit 5G and IoT in many ways, including optimizing some 5G enabled technologies such as cloud computing and smart cities. In this paper, discussion about the overall integration of 5G, Blockchain and IoT will be mainly focused on. Firstly, the definition and motivation of this “5G Blockchain-IoT” integration will be introduced; Secondly, the contributions of the integration on 5G enabled technologies and applications will be discussed and listed; Thirdly, challenges, issues and future perspectives of the blockchain, 5G and IoT integration will also be estimated.