Reviewerly/exHarmony huggingface dataset:
Developing an Audit and Feedback Dashboard for Family Physicians: A User-Centered Design Process

<h3>Context:</h3> Audit and Feedback (A&amp;F), the summary and provision of clinical performance, is a popular quality improvement strategy. We are developing a web-based dashboard that uses data from the electronic medical record to help physicians identify gaps in care and act. However, A&amp;F tools can only be effective if the targeted health professionals actively review their data and take action. In order to maximise the impact of A&amp;F, the design should consider the needs and goals of clinicians. <h3>Objective:</h3> To describe the development of a user-centered design process to optimize the effect of an A&amp;F dashboard for family physicians. <h3>Study Design and Analysis:</h3> Our design process includes (1) Prototype development based on A&amp;F theory and input from clinical improvement leaders; (2) a co-creation workshop with family physician quality improvement leaders to develop personas (i.e., fictional characters that represent an archetype character); (3) user-centered interviews with family physicians to learn about the physician’s who will be using the dashboard and their context, and their reactions to the dashboard. <h3>Setting or Dataset:</h3> A workshop for the creation of personas and user-centered qualitative interviews with family physicians. <h3>Population Studied:</h3> Family physicians who contribute data to the University of Toronto Practice-Based Research Network <h3>Intervention/Instrument:</h3> Audit and Feedback dashboard <h3>Outcome Measures:</h3> N/A <h3>Results:</h3> Our persona workshop produced four personas that enabled the team to identify physician’s needs and wishes and facilitated empathy during the design process: Dr. Skeptic, Frazzled Physician, The Eager Implementer, and Sidney Big Wig. Our interviews found that: (1) physicians are interested in how they compare with their peers; however, if their performance was above average, they were not motivated to improve even if gaps in care remained; (2) Burnout levels are high, physicians are trying to catch up on missed care during the pandemic, and were not highly motivated to act on the dashboard data; (3) Features that were important to physicians were integration within the EMR, and up-to-date and accurate data. <h3>Conclusions:</h3> A successful design of an A&amp;F performance dashboard should consider the serious lack of time and capacity among family physicians to engage in quality improvement work. If designed properly, the QI dashboard can be a great assistance in helping family physicians provide more proactive and targeted care.
Fragile Watermarks Detecting Forged Images

Technology and digital communications have advanced so that digital photos, videos, or text may be easily manipulated by those not authorized to do so. In addition, the availability of specialized picture editing programs like Photoshop has simplified the process of altering photographs. At first glance, there may seem to be no problem, especially when an image editing method is necessary to delete or add a certain scene that increases the picture’s beauty. But what about personal images or images with copyright? Attempts are constantly made to spoof these images using different approaches. Therefore, measures to reduce the likelihood of counterfeiting in digital and printed forms of media are required. The proposed approach aims to detect a counterfeit in images using a unique generator that conceals the data represented by the embedded watermark utilizing modern visual cryptography and hash algorithms. Image extractions may easily be analyzed for signs of forgery. As a result, our approach will detect and validate phony documents and images.
Non-interactive Mimblewimble Transactions, Revisited

Mimblewimble is a cryptocurrency protocol that promises to overcome notorious blockchain scalability issues and provides user privacy. For a long time its wider adoption has been hindered by the lack of non-interactive transactions, that is, payments for which only the sender needs to be online. Yu proposed a way of adding non-interactive transactions to stealth addresses to Mimblewimble, but this turned out to be flawed. Building on Yu and integrating ideas from Burkett, we give a fixed scheme and provide a rigorous security analysis strenghtening the previous security model from Eurocrypt’19. Our protocol is considered for implementation by MimbleWimbleCoin and a variant is now deployed as MimbleWimble Extension Blocks (MWEB) in Litecoin.
Introduction of Probability Distributions

This chapter introduces several probability distributions that are commonly used in statistical modeling. We explain the basic properties of these fundamental distributions, and further provide some examples to explain how to use them in practice, and other information that should be taken into account when building a model with them.
Hubungan antara Efikasi Diri dengan Kecurangan Akademik pada Siswa Kelas XI IPA dan IPS di SMA Negeri I Tanjung Batu

This study discusses the relationship of self-efficacy and academic cheating in class XI science and social studiesat SMA Negeri 1 Tanjung Batu. This study aims to determine whether there is a relationship of self-efficacydengan academic cheating in class XI science and social at SMA Negeri 1 Tanjung Batu. By using quantitativeresearch. The sample used in the study amounted to 123 student. Data analysis method used is product momentanalysis. Based on the result of research that has done, it can be concluded that there is a no relationship betweenself-efficacy and academic cheating, this is evidenced by the result of the correlation coefficient which show thenumber -0.156 with a sig. 0.085 where p &gt; 0.05. Meaning that the higher the self-efficacy, the lower the academiccheating conversely the lower of self-efficacy the higher the academic cheating.
How to train a self-driving vehicle: On the added value (or lack thereof) of curriculum learning and replay buffers

Learning from only real-world collected data can be unrealistic and time consuming in many scenario. One alternative is to use synthetic data as learning environments to learn rare situations and replay buffers to speed up the learning. In this work, we examine the hypothesis of how the creation of the environment affects the training of reinforcement learning agent through auto-generated environment mechanisms. We take the autonomous vehicle as an application. We compare the effect of two approaches to generate training data for artificial cognitive agents. We consider the added value of curriculum learning-just as in human learning-as a way to structure novel training data that the agent has not seen before as well as that of using a replay buffer to train further on data the agent has seen before. In other words, the focus of this paper is on characteristics of the training data rather than on learning algorithms. We therefore use two tasks that are commonly trained early on in autonomous vehicle research: lane keeping and pedestrian avoidance. Our main results show that curriculum learning indeed offers an additional benefit over a vanilla reinforcement learning approach (using Deep-Q Learning), but the replay buffer actually has a detrimental effect in most (but not all) combinations of data generation approaches we considered here. The benefit of curriculum learning does depend on the existence of a well-defined difficulty metric with which various training scenarios can be ordered. In the lane-keeping task, we can define it as a function of the curvature of the road, in which the steeper and more occurring curves on the road, the more difficult it gets. Defining such a difficulty metric in other scenarios is not always trivial. In general, the results of this paper emphasize both the importance of considering data characterization, such as curriculum learning, and the importance of defining an appropriate metric for the task.
Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.
Research on Trajectory Planning of Robotic Arm Based on RRT Algorithm

Taking the 6-DOF manipulator as the research object, the coordinate system of the manipulator link is established by the D-H parameter method, and the forward and inverse kinematics equations of the manipulator are derived through the ma-trix transformation relationship between the link coordinate systems. The end-of-arm controller performs motion trajectory planning. In Matlab, Robotic toolbox is used to model and analyze the forward and inverse kinematics of the manipulator, and the improved RRT algorithm trajectory and the manipulator model are used for joint motion simulation. The simulation results prove the reliability of the ma-nipulator model and the effectiveness of the improved RRT algorithm. Feasibility.
Manipulator Control System Based on Human Posture Recognition

Given that Openpose [1] is too difficult to deploy and the model is too large.This project uses MediaPipe, which is an easy-to-deploy package of deep learning proposed by google to extract human skeleton coordinate points and output real-world 3D coordinates based on the abdomen. A four-layer neural network is constructed as the limb signal classifier to classify the obtained human body three-dimensional coordinates and complete the limb signal classification. The mechanical arm is modeled by D-H [2] method, the forward and inverse kinematics formulas are deduced to determine the working space of the mechanical arm. Establish the mathematical model for the movement of the lifting robot from the current position to the target position.After collecting the human body image with Kinect DK, it is processed by neural network as the input signal of the mechanical arm. The mechanical arm communicates with the industrial control PC through TCP\IP communication protocol cluster, and finally completes the function of human body posture controling mechanical arm.
Preface

Abstract The 2nd TALENTA-International Conference on Science and Technology (TALENTA-ICST) was planned to be held on 3rd December 2020 in Medan, Indonesia. Due to the high cases of Covid-19, the organizing committee decided this conferece was held virtually as countries around the world are now issuing restrictions on travel, gatherings, and meetings in an effort to limit and slow the spread of this pandemic. The health and safety of our participants and members of our research community is of top priority to the Organizing Committee. Therefore, TALENTA-ICST 2020 was held online through Zoom Meeting. TALENTA-ICST 2020 has become a premier forum for discussion, dissemination and achievement of research in the related fields. It bring innovative academics, researchers, and students as well as industrial experts in the field of science, engineering and technology. It was attended by more 150 participants virtually. During the conference, the conference model was divided into two sessions, including keynote speeches and oral presentation (Including Q&amp;A discussion). The first part, keynote speakers were each allocated 30 - 45 minutes to hold their speeches. After speech, the moderator had opened the Q&amp;A discussion between speaker and participant. The second part, committee had devided some virtual room based on the similairty of the presented articles. The presenters had 10 - 15 minutes to present their research and then 5 minutes for Q&amp;A discussion. In today’s conference, TALENTA ICST are honored to accept 70 outstanding works of authors that have been through a challenging review process. With the acceptance rate of 60.3%, it is unfortunate for us to see that some high-quality manuscripts did not manage to go through the review process. The review process includes the plagiarism checking and the content reviewing by the reliable reviewers. List of Committees are available in this Pdf.
Performance Evaluation of Resnet Model on Sign Language Recognition

Communication is an important tool for sharing one's ideas and thoughts and as such its role in our everyday lives cannot be over emphasised.Sign language is a form of communication used by the deaf and those hard-of-hearing.However, a challenge arises when deaf people have to communicate their ideas to those in the mainstream population.An automatic translator can be an effective way to address this problem.In this study, the performance of the ResNet model and its variants are evaluated on two different datasets.The first dataset contains images of American Sign language (ASL) data and the second dataset consists of images of Indian Sign language (ISL).The is a one-handed sign language, while ISL is mainly a two-handed sign language with complex shapes.ResNet variants such as Resnet18, ResNet34, ResNet50, ResNet101 and ResNet152 have been tested on these standard datasets.We conducted experiments by using deep neural networks to make recommendations and predictions in sign language.Experimental results using a standard dataset demonstrate that the model with 152 layers achieves the highest accuracy.
2023 IEEE CIS Awards [Society Briefs]

Presents the recipients of CIS awards for 2023.
PENERAPAN METODE SAW DALAM MENENTUKAN KEPUTUSAN SUSPEK POSITIF COVID-19

Research on Decision Support Systems that can detect symptoms of positive Covid-19 patients. This study aims to help health workers apply IT in services to Covid-19 patients. Data taken from samples obtained directly from the Riau Provincial Health Office and Pekanbaru City Health Office and the official website of the National Covid-19 Management Agency. In this research, the team consists of the fields of computer science and psychology education in order to fulfill the aspects needed in improving the research results. The assessment aspect of the recommended criteria for Covid-19 tracing patients are 11 questions taken from the Riau Province Covid-19 website. The data is weighted and processed by SAW method Decision Support Systems. The results of the method processing is 3 decision recommendations that are indicated by Covid-19.
Adaptive boosting for ordinal target variables using neural networks

Abstract Boosting has proven its superiority by increasing the diversity of base classifiers, mainly in various classification problems. In reality, target variables in classification often are formed by numerical variables, in possession of ordinal information. However, existing boosting algorithms for classification are unable to reflect such ordinal target variables, resulting in non‐optimal solutions. In this paper, we propose a novel algorithm of ordinal encoding adaptive boosting (AdaBoost) using a multi‐dimensional encoding scheme for ordinal target variables. Extending an original binary‐class AdaBoost, the proposed algorithm is equipped with a multi‐class exponential loss function. We show that it achieves the Bayes classifier and establishes forward stagewise additive modeling. We demonstrate the performance of the proposed algorithm with a base learner as a neural network. Our experiments show that it outperforms existing boosting algorithms in various ordinal datasets.
Dis-Spiracy Theory

the Canadian newspaper The Globe and Mail in collaboration with the news organization Tortoise named the Indonesian musician Jerinx the world's fifth-largest "super-spreader" of online medical misinformation.With an online following of close to a million in mid-2020, the Instagram and Twitter posts by the drummer of the Balinese punk-rock band Superman Is Dead generated more than 80,000 interactions, an online impact that was surpassed only by that of the US environmental lawyer and anti-vaxxer Robert Kennedy Jr., the Californiabased Scientologist Rizza Islam, the Nigerian politician Femi-Fani-Kayode, and the Samoan-Australian natural health influencer Taylor Winterstein (Hollowood, Serato, and Newell 2020).Jerinx (pronounced Jerink) appears to fit the standard figure of the Covid-19 conspiracy theorist.And yet, as this chapter will show, Jerinx's conspiratorial posts partake in a broader political discourse about the occult side of power that appeals to police commissioners as much as punks and to ministers as much as ex-magicians.Beginning with a short description of the conspiracy theories of Jerinx, the chapter moves on to dissolve these theories in a political aesthetic that takes the form of "collusion theory" and "punk-spiracy" and to trace the similarities to other theories promoted by influential figures such as police commissioner Dharma Pongrekun and former Minister of Health Siti Fadilah Supari.The chapter concludes by suggesting the need for a "dis-spiracy theory" analysis, an analytical attempt to focus on the disparate overlaps between conspiratorial discourses and general discourses of political reason in a given context. Your Average Conspiracy Thinker?Jerinx, whose given name is I Gede Ari Astina, is a seemingly self-evident instance of a "super-spreader" of viral misinformation about the Covid-19 pandemic.
Medical image analysis for the early prediction of hypertension

Recently, medical image analysis has become a vital evolving technology that is used in the early diagnosis of various diseases. Medical imaging techniques enable physicians to capture noninvasive images of structures inside the human body (such as bones, tissues, or blood vessels) as well as their functions (such as brain activity). In this study, magnetic resonance angiography (MRA) images have been analyzed to help physicians in the early prediction of hypertension. Hypertension is a progressive disease that may take several years before being fully understood. In the United States, hypertension afflicts one in every three adults and is a leading cause of mortality in more than half a million patients every year. Specific alterations in human brains’ cerebrovasculature have been observed to precede the onset of hypertension. This study presents a computer-aided diagnosis system (CAD) that can predict hypertension prior to the systemic onset of the disease. This MRA-based CAD system is able to detect, track, and quantify the hypertension-related cerebrovascular alterations, then it makes a decision based on the analyzed data about whether each subject is at a high risk of developing hypertension or not. Such kind of prediction can help clinicians in taking proactive and preventative steps to stop the progress of the disease and mitigate adverse events.
Comparison between optical markers and physical markers-assisted panoramic 3D measurement

Panoramic 3D measurement becomes increasingly important for fringe projection profilometry (FPP). Traditional physical markers-assisted (PMA) method suffers from inefficiencies and non-complete measurement. An optical markers-assisted (OMA) panoramic 3D method has been recently proposed, which enables accurate, efficient and non-destructive panoramic 3D measurement. In this paper, we give a comprehensive comparison between OMA and PMA, which provides reasonable suggestions for different panoramic 3D measurement applications.
Validity analysis of four-surface measurement algorithm based on wavelength-shifting interferometry

Transparent parallel flat optical components are found everywhere and used in a wide range of applications. Surface topography and inhomogeneity distribution are important physical parameters for the components. In order to improve the performance and reliability of components, topography should be measured with high accuracy. The weighted multi-step sampling algorithm based on wavelength-shifting interferometry is an effective method for multi-surface topography recovery, which is often based on the three-surface measurement and has poor accuracy for rear-surface measurement. The four-surface measurement can compensate for this drawback, but the system and the solving process are more complex. There are fewer studies on the analysis of algorithm effectiveness. In this paper, through theoretical derivation, it is proved that the validity of the four-surface measurement algorithm is only related to the recovery of three sets of interference signals. The distribution of the wavefront residuals RMS values under different combinations of phase shift coefficients and cavity length coefficients are used to summarize the algorithm error distribution law and the best matching database of phase shift parameters under four-surface measurement is established. The effective cavity length coefficients and phase shift coefficients are selected to compare the wavefront recovery under three-surface and four-surface, and it is verified that the measurement accuracy of the four-surface is better when effective parameters are selected.
Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System

Pedestrian safety has become a critical consideration in developing society especially road traffic, an intelligent transportation need of the hour is the solution left. India tops the world with 11% of global road accidents. With this data, we have moved in the direction of computer vision applications for efficient and accurate pedestrian detection for intelligent transportation systems (ITS). The important application of this research is robot development, traffic management and control, unmanned vehicle driving (UVD), intelligent monitoring and surveillance system, and automatic pedestrian detection system. Much research has focused on pedestrian detection, but sustainable solution-driven research must still be required to overcome road accidents. We have proposed a wireless sensor network-based pedestrian detection system that classifies the real-time set of pedestrian activity and samples the reciprocally received signal strength (RSS) from the sensor node. We applied a histogram of oriented gradient (HOG) descriptor algorithm K-nearest neighbor, decision tree and linear support vector machine to measure the performance and prediction of the target. Also, these algorithms have performed a comparative analysis under different aspects. The linear support vector machine algorithm was trained with 481 samples. The performance achieves the accuracy of 98.90%and has accomplished superior results with a maximum precision of 0.99, recall of 0.98, and F-score of 0.95 with 2% error rate. The model’s prediction indicates that it can be used in the intelligent transportation system. Finally, the limitation and the challenges discussed to provide an outlook for future research direction to perform effective pedestrian detection.