Reviewerly/exHarmony huggingface dataset:
Evaluating the Effects of a Mindfulness Mobile Application on Student Pharmacists’ Stress, Burnout, and Mindfulness

Pharmacists report high levels of burnout. Mindfulness approaches have been demonstrated to positively affect the general population and other healthcare professions. However, limited studies have been performed to evaluate mindfulness approaches in student pharmacists. This study aimed to evaluate the effectiveness of the daily use of a mindfulness mobile application in improving student pharmacists’ perceived stress, burnout, and mindfulness.
Enrichment Materials Including Simple Gamification to Enhance Learning Pharmacy Law

To determine if use of optional enrichment improved student performance in a pharmacy law course.
A Qualitative Analysis of an Interprofessional Musical Educational Workshop Focusing on Non-Verbal Communication Skills

The importance of non-verbal communication on patient care has been well established in the literature. However, few health professional curricula teach these skills. Musicians employ non-verbal communication to coordinate their performances. Observation of musicians' use of non-verbal communication may improve the understanding and acquisition of these skills by healthcare professional students.
Unveiling Social Relations: Leveraging Interpersonal Similarity Learning for Social Relation Recognition

Identifying social relationships from images is a challenging yet promising research area with great potential for improving human health and enhancing our understanding of social networks. However, present endeavors in this field tend to concentrate on leveraging visual features for the exploration of social relationships, while disregarding certain concealed information that lies beneath these features, such as interpersonal similarity. These methodologies may result in inadequate visual data encoding, thereby imposing limitations on the accuracy of social relationship recognition. In light of this, we propose a novel framework that utilizes interpersonal similarities within images to provide more information for identifying social relationships, thereby mitigating the issue of insufficient feature exploration. Furthermore, our proposed framework incorporates an innova-tive CF-Loss function that effectively incentivizes the identifica-tion of accurate social relationships while penalizing incorrect identifications, ultimately bolstering the model's capacity to dis-criminate between distinct social relationships. Our experimental findings demonstrate the superiority of our proposed framework over state-of-the-art methods on public datasets, confirming its effectiveness and accuracy in identifying social relationships.
A Novel Traffic Obfuscation Technology for Smart Home

With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.
Elimination of Herbicides after the Classification of Weeds Using Deep Learning

Introduction: Herbicides are chemicals that are used to destroy weeds. It is commonly used in agriculture to kill undesired plants and increase crop yield, even though it has negative effects on humans and the environment. Pesticides sprayed on crops must be decreased in the real world to protect humans, animals, and birds from dangerous diseases such as cancer, eyes, and skin infection. Pesticides are classified as herbicides. Deep learning is being used in this research to minimize chemical compounds. Scientists seek to limit the amount of pesticide sprayed on crops to protect humans and the environment from toxic exposure. Background: In this research, A neural network classifier is built using Convolutional Neural Network (CNN), dropout, rectified linear activation unit (ReLU), the Root Mean Squared Propagation (RMSprop) optimization technique, and stochastic gradient descent (SGD). The algorithms based on CNN outperformed the others. This study uses generated dataset (unique dataset and processes it rowwise through the Neural network) to train a categorized neural network, and the dataset was created with the assistance of the agriculture professor. Methods: This study offers a method for classifying weed images and spraying herbicides solely on weeds/unwanted plants rather than crops. The model should first be trained using the training dataset before being tested using the testing datasets. Results: This model's training accuracy is 96%, while testing accuracy is 89%. Conclusion: This model reduced herbicide (it is a type of pesticide/chemical) spray over the crop (foods, vegetables, sugarcane) to protect humans, animals, birds, and the environment from harmful chemicals.
Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Background Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively. Objective This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions. Method LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist. Results Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting. Conclusion To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/ , identifier: CRD42021272198.
ANALISA KETAHANAN DAN KINERJA PANEL SURYA DI KAWASAN PESISIR

The coastal area is an area that is high in salt content so that it can corrode the tools around it. As with solar panels in Solar Power Plants if installed in coastal areas, their performance will likely decrease. Although Solar Power Plants is a renewable energy that is obtained free of charge from sunlight, the solar panel attached to Solar Power Plants is also a tool or instrument which functions to create electric power. The hypothesis that has been made is that solar panels have a decreased performance when in coastal areas compared to solar panels installed in urban areas. In this study, a comparison or analysis of the performance of solar panels in coastal areas with urban areas will be discussed. Therefore, from this analysis, it is hoped that it can draw a conclusion that there is special or extra care when installing Solar Power Plants in coastal areas.
A non-global disturbance targeted adversarial example algorithm combined with C&amp;W and Grad-Cam

Adversarial examples are artificially crafted to mislead deep learning systems into making wrong decisions. In the research of attack algorithms against multi-class image classifiers, an improved strategy of applying category explanation to the generation control of targeted adversarial example is proposed to reduce the perturbation noise and improve the adversarial robustness. On the basis of C&W adversarial attack algorithm, the method uses Grad-Cam, a category visualization explanation algorithm of CNN, to dynamically obtain the salient regions according to the signal features of source and target categories during the iterative generation process. The adversarial example of non-global perturbation is finally achieved by gradually shielding the non-salient regions and fine-tuning the perturbation signals. Compared with other similar algorithms under the same conditions, the method enhances the effects of the original image category signal on the perturbation position. And it makes up for the shortcomings of the adversarial example algorithm in terms of interpretability and teaching intuitiveness. Experimental results show that the improved adversarial examples have higher PSNR. In addition, in a variety of different defense processing tests, the examples can keep high adversarial performance and show strong attacking robustness.
End-to-end affine registration framework for histopathological images with weak annotations

Histopathological image registration is an essential component in digital pathology and biomedical image analysis. Deep-learning-based algorithms have been proposed to achieve fast and accurate affine registration. Some previous studies assume that the pairs are free from sizeable initial position misalignment and large rotation angles before performing the affine transformation. However, large-rotation angles are often introduced into image pairs during the production process in real-world pathology images. Reliable initial alignment is important for registration performance. The existing deep-learning-based approaches often use a two-step affine registration pipeline because convolutional neural networks (CNNs) cannot correct large-angle rotations. In this manuscript, a general framework ARoNet is developed to achieve end-to-end affine registration for histopathological images. We use CNNs to extract global features of images and fuse them to construct correspondent information for affine transformation. In ARoNet, a rotation recognition network is implemented to eliminate great rotation misalignment. In addition, a self-supervised learning task is proposed to assist the learning of image representations in an unsupervised manner. We applied our model to four datasets, and the results indicate that ARoNet surpasses existing affine registration algorithms in alignment accuracy when large angular misalignments (e.g., 180 rotation) are present, providing accurate affine initialization for subsequent non-rigid alignments. Besides, ARoNet shows advantages in execution time (0.05 per pair), registration accuracy, and robustness. We believe that the proposed general framework promises to simplify and speed up the registration process and has the potential for clinical applications.
Test rig for comparison of particle types for 3D optical trap displays

In this work we report an improved platform for testing and comparing particles for use in optical trap displays. We constructed seven prototypes, and deployed them to five different locations where they were successfully used to perform comparative optical particle trap tests. This improved rig makes it possible to expand optical trap display research by a decentralized group of citizen scientists.
Pelatihan Peningkatan Keterampilan Belajar Peserta Didik Menggunakan Aplikasi Canva di Sekolah Perbatasan Indonesia-Malaysia

This service activity is to improve students' learning skills using the Canva application at SMA Negeri 1 Jagoi Babang, Bengkayang Regency (Indonesia – Malaysia Border School). The method of implementing this service uses training and assistance in making products. The evaluation instrument uses survey percentages and is analyzed descriptively. The results of this service show that training activities using the Canva application provide positive benefits and impacts for students, where students can understand the Canva application as an application that can support it in learning activities. Students are able to use this application and produce output in the form of design products, one of which is presentation material.
Pelatihan Pengolahan Data Penelitian Pendidikan IPA Menggunakan STATA Data Analysis

The community service aims to increase the capacity of Universitas Qamarul Huda Badaruddin (UNIQHBA) lecturers in processing and analyzing science education research data using STATA Software. The method of community service was used for interactive training for two weeks with the Zoom application for 37 lecturers from UNIQHBA. To determine the participants' ability to use the STATA software, practice processing and data analysis were carried out using a science education case study guide provided by the facilitator. The evaluation was carried out at the end of the training to get participants' responses through online questionnaires that were then analyzed descriptively. The results of this community service were that as many as 89.32% of the participants were able to complete the practice correctly based on the assignments given, 89.74% of the presentations given by the facilitator were clear and easy to understand and will use STATA for the processing and analysis of their research data because it was easy to use. Most of the participants recommended further training using the STATA Software.
Tinjauan Atas Sistem Penerimaan Kas Dan Pengeluaran Kas Pada Klub Golf Bogor Raya

ABSTRACTIn the process of cash receipts and cash disbursements, it is necessary to have an appropriate system so that the implementation can be carried out smoothly. As with cash receipts and cash disbursements, they must have complete books and records so that the cash receipts system and cash disbursement system can be controlled properly.The purpose of the discussion in this study is to find out how the cash receipts system and cash disbursement system exist at PT. Bogor Raya Development (Bogor Raya Golf Club). To achieve clear and directed discussion targets, the authors formulate problems regarding the Cash Receipt System, Cash Disbursement System, the constraints that arise in the implementation of cash receipts and cash disbursements, and how the solution is to resolve these obstacles.The results that have been discussed show that cash receipts at PT. Bogor Raya Development (Bogor Raya Golf Club) is sourced from cash sales or payments by receivables from restaurants and proshops, payment of golf membership fees and from wedding event reservations. While cash disbursements at PT. Bogor Raya Development (Bogor Raya Golf Club) is carried out only for the company's operational activities and is recorded in the company's bookkeeping system.Keywords : Cash, Cash Receipt, Cash Disbursement.
Development of practicum guideline active compound isolation base on medical plant in Sasambo Society, Indonesia

The people of NTB have many distinctive cultures and cuisines. The use of plants is traditional medicine. This study aims to develop practical guidelines based on the medicinal plant sasambo, NTB. Development design is Research &amp; Development with a 4D model (define, design, develop, and disseminate). The results of laboratory experiments are outlined in practicum guidelines for student activities. Chemistry lecturers in chemistry and natural product chemistry constructively validate the procedures. The analytical method uses Aiken's V formula and percentage agreement. The results of the expert validation show that the practicum guidelines meet very valid criteria with an Aiken value of 0.86. The guidelines also received positive responses from students and expert validators with an average practicality level of 88.6% with very practical criteria. Guidelines can be applied to learning with a percentage agreement of 100%. The cultural richness applied to learning can increase motivation learning and encourage students to be creative in developing products from the Sasambo milk medicinal plant.
Analysis of the Effectiveness of Implementing Fingerprint Absence, Discipline and Motivation on the Performance of Mawing I Paskhas Lanud Halim Perdana Kusuma Jakarta

The purpose of this study was to analyze the effect of the effectiveness of the application of fingerprint attendance and discipline on the performance of members of Mawing I Paskhas Halim Perdana Kusuma Jakarta, either simultaneously or partially. This study also wants to analyze which variables of the effectiveness of the application of fingerprint attendance and discipline have a dominant effect on the performance of the members of Mawing I Paskhas Halim Perdana Kusuma Jakarta. This type of research is explanatory research. The study population was all members of the NCO Mawing I Paskhas Halim Perdana Kusuma Jakarta, as many as 50 members. Considering that the population is not too large, census research is used, namely all members of the NCO Mawing I Paskhas Halim Perdana Kusuma Jakarta will be research respondents. The analysis method uses multiple linear regression and hypothesis testing. The results showed that the effectiveness of the application of fingerprint absence, discipline and motivation to the performance of members of the Mawing I Paskhas Halim Perdana Kusuma Jakarta, both simultaneously and partially. Furthermore, this study identified that the effectiveness of the application of fingerprint absence had a dominant effect on the performance of members of Mawing I Bintara Paskhas Halim Perdana Kusuma Jakarta.
Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation

Observational medical data present unique opportunities for analysis of medical outcomes and treatment decision making. However, because these datasets do not contain the strict pairing of randomized control trials, matching techniques are to draw comparisons among patients. A key limitation to such techniques is verification that the variables used to model treatment decision making are also relevant in identifying the risk of major adverse events. This article explores a deep mixture of experts approach to jointly learn how to match patients and model the risk of major adverse events in patients. Although trained with information regarding treatment and outcomes, after training, the proposed model is decomposable into a network that clusters patients into phenotypes from information available before treatment. This model is validated on a dataset of patients with acute myocardial infarction complicated by cardiogenic shock. The mixture of experts approach can predict the outcome of mortality with an area under the receiver operating characteristic curve of 0.85 ± 0.01 while jointly discovering five potential phenotypes of interest. The technique and interpretation allow for identifying clinically relevant phenotypes that may be used both for outcomes modeling as well as potentially evaluating individualized treatment effects.
Power Information System Alarm Noise Reduction Technology Based on Graph Theory and Information Entropy

In order to deal with the redundancy, irrelevance and repetition of massive alarm information in power information system, this paper proposes an alarm noise reduction technology based on graph theory clustering and information entropy. Based on the topological structure of the device and the correlation between the alarms, the node-edge model is established. The CH index (Calinski-Harabasz) method is used to find the appropriate number of clusters to cluster the alarm information. The information entropy method is used to optimize and redundancy. Finally, the average output alarm of each system accounts for 36.63% of the original alarm. The proposed method can effectively mine the similarity and difference of alarm information, automatically and dynamically classify alarms, reduce misjudgment, help network administrators of power grid enterprises to screen out core alarm information, and improve management efficiency and quality.
Contour Completion by Transformers and Its Application to Vector Font Data

In documents and graphics, contours are a popular format to describe specific shapes. For example, in the True Type Font (TTF) file format, contours describe vector outlines of typeface shapes. Each contour is often defined as a sequence of points. In this paper, we tackle the contour completion task. In this task, the input is a contour sequence with missing points, and the output is a generated completed contour. This task is more difficult than image completion because, for images, the missing pixels are indicated. Since there is no such indication in the contour completion task, we must solve the problem of missing part detection and completion simultaneously. We propose a Transformer-based method to solve this problem and show the results of the typeface contour completion.
Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer Security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification.