Reviewerly/exHarmony huggingface dataset:
Deep learning based normative models identify structures that become abnormal with Alzheimer’s disease

Abstract Background Improving dementia diagnosis is a major global concern, given that up to 20% of subjects are misdiagnosed. Normative modelling, which quantifies how far individuals deviate from healthy controls (HC), may help to better situate subjects along the spectrum. To determine whether this is the case, we implemented a deep learning based normative model and tested it on structural neuroimaging data from subjects with HC, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Method We developed an adversarial autoencoder (AAE) ( Figure 1 ) leveraging FreeSurfer features, namely cortical surface area of 68 cortical subregions (34 by hemisphere; Desikan‐Killiany atlas) and volumes of 33 neuroanatomical structures (Aseg atlas). Our model not only returns individual “deviation” scores—divergence from HC—but also identifies regions of interest that contribute to said score, facilitating interpretability. We retrieved T1‐weighted MRI scans (n = 204) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (HC/MCI/AD 105/29/60; Table 1 ). We trained our AAE on 71 HC samples (n = 71) and tested it on the rest (n = 133). We used bootstrapping to compute confidence intervals (CI) for each point estimate and examined regions where scores differed for the considered groups [1]. Result Deviation scores were lowest for HC (mean 0.74 [95%‐CI 0.63–0.88]), highest for AD (mean 1.21 [95%‐CI 1.03–1.50]), and intermediate for MCI (mean 1.04 [95%‐CI 0.88–1.27]), implying subjects indeed fell along the AD spectrum ( Figure 2A ). Said scores also enabled classifying subjects into HC, MCI, and AD with a mean area under the curve (AUC) of 75% ( Figure 2B‐C ; HC vs. MCI: 0.75 [95%‐CI 0.62–0.85]; HC vs. AD: 0.76 [95%‐CI 0.64–0.82]). According to the AAE, the hippocampus, parahippocampal cortex, ventricles, and inferior parietal lobe contributed to such differences ( Figure 2D‐E ). Conclusion Normative models can identify structures that become abnormal as the brain undergoes ageing and AD, in line with the literature. Further testing on a larger and more heterogeneous sample will reveal the potential of this tool and its potential deployment in clinics. References : [1] W. H. L. Pinaya et al., “Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross‐sectional multi‐cohort study”, Scientific reports, vol. 11 1, p. 15746, 2021.
Determining the Law of Reading Tajweed (Idgham Qomariyah and Syamsiyah) in the Qur'an Using the Naïve Bayes Algorithm

Learning tajweed to recite the Qur’an is important. Because if there is a mispronunciation, the meaning will be different. This research aims to detect two of the many tajweed, namely idghom qomariyah and syamsiyah using machine learning technology with a classification approach. This research uses the Naive Bayes algorithm to classify idghom qomariyah and syamsiyah in Al-Quran text documents. Based on experimental results using 82,173 text data, Naive Bayes was able to classify idgham qomariyah and syamsiyah with an accuracy rate of 96,80%.
Prediction of Skin Diseases using Convolutional Neural Networks as an Effort to Prevent Their Spread in Islamic Boarding School Environments

Skin disease is a common health problem in Islamic boarding school environments. This disease can spread quickly among students due to close contact and sharing the same facilities. Preventing the spread of skin diseases is a top priority in efforts to maintain the health and welfare of students in Islamic boarding schools. In this research, we propose the use of machine learning techniques to predict skin diseases in Islamic boarding school students. The main goal of this research is to develop a predictive model that can help identify skin diseases quickly and accurately. It is hoped that this will enable the prevention of the spread of skin diseases in the Islamic boarding school environment. The method used in this research involves the following steps: skin disease image data collection, data processing and cleaning, feature extraction from patient data, and machine learning model training and evaluation. We will use a Convolutional Neural Network (CNN) machine learning algorithm to build a predictive model. The dataset used in this research consists of images of melanoma, acne and acne skin diseases. In addition, validation will be carried out using data that has never been seen before to test the performance of the predictive model. It is hoped that the results of this research can make a significant contribution in preventing the spread of skin diseases in the Islamic boarding school environment. With accurate predictive models, health workers in Islamic boarding schools can take appropriate preventive measures to control skin diseases effectively. Apart from that, this research can also be a basis for developing a health information system that supports preventive measures for skin diseases in Islamic boarding schools more widely.
Exploring Social Engineering Attacks Using Spear Phishing in a Universtiy

A thorough investigation of social engineering attacks was performed on the lab environment. Numerous users can be easily attacked through the use of illegitimate emails that may come from trusted users. Organizations often try to implement security measures but they remain susceptible to these attacks. The purpose of this study was to show that university students are at risk of being attacked. The deployment of a dedicated lab environment and the susceptibility of the network to countless social engineering attacks were completely assessed in a controlled lab environment. The targeted audience, students, showed that they were more vulnerable. The findings of these investigations revealed that the network is vulnerable to social engineering attacks, specifically spear phishing attempts. Recommendations were made that included that universities should invest in educating students, staff members, and the faculty at large about certain threats and how to avoid falling prey to them.
Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing

This research aims to make it easier to memorize the Koran without having to need other people. Memorizers of the Koran (hafiz) often need other people to memorize them to find out if there are errors in their reading. Therefore, this research utilizes machine learning technology to make it easier to read and memorize the Al-Qur'an using the Convolutional Neural Network (CNN) algorithm. CNN was chosen because it is very good at classifying images and audio and can learn and extract features from raw data, such as image and audio data automatically. As a result, the model created succeeded in distinguishing one verse from another very well. The validation results show that the model can correctly detect 57 verses from 64 recorded data, which means it has an accuracy rate of 89.06%. With this verse classification model, it can then be implemented into an application to help memorize the Al-Qur'an even without using the internet.
Suspicious Activity Detection

Abstract: Unpredictable activities is guessing a person's physical location or joint activity based on images or videos. The project will require the use of neural networks to detect human activities from CCTV images. Human behavior is one of the fundamental problems in computer vision and has been studied for over 15 years. This is important because the number of applications that can benefit from the search function is huge. For example, it is used in applications such as human prediction, video analysis, animal analysis and behavior understanding, language recognition, human relations-computer, and characters are less powerful. While low-cost depth sensors have limitations such as being limited to indoor use, their low-noise data and low depth make it difficult to estimate humans from depth images. Therefore, we plan to use neural networks to solve these problems. Analyzing human activity through image analysis is an active area of research in image processing and computer vision. Thanks to visual monitoring, human activities in public places such as bus stops, train stations, airports, banks, shopping malls, schools, parking lots and roads can be monitored in order to prevent attacks, theft, accidents, illegal parking, violence, fights and chain events. purse snatching etc. illegal and other activities. It is very difficult to monitor public places regularly, so there is a need for intelligent video surveillance that can monitor people's activities in real time and divide them into tasks, normal and abnormal activities; and can generate an alarm. Often research is done with images rather than video. Also, none of the published articles attempt to use CNNs to detect suspicious activity.
Predicting cryptocurrency investment suitability using machine learning techniques

The study aims to predict the close prices of four different cryptocurrencies (Bitcoin, Ethere-um, Dogecoin, and Cardano) using machine learning techniques and determine which of these cryptocurrencies is suitable for investment. To achieve this goal, we used two popular gradi-ent boosting algorithms: Extreme Gradient Boosting (XGBoost) and Light Gradient-Boosting Machine (LightGBM). Prediction accuracy of the trained model is evaluated by Mean Abso-lute Error (MAE) generated by the methodology of Cross-Validation. Our results show that both XGBoost and LightGBM can effectively predict the close prices of the four cryptocur-rencies, with LightGBM achieving slightly better performance in terms of prediction accura-cy. Based on our analysis, we were able to identify which cryptocurrencies were suitable for investing and provide recommendations for potential investors. Overall, our study highlights the potential of machine learning techniques in predicting cryptocurrency close prices and identifying suitable investment opportunities.
Contactless fall detection with radio frequency sensors for the aging population

Abstract Background Falls remain a primary concern for People with Dementia (PwD) living in long‐term care facilities (LTCFs) despite many prevention and intervention methods that have tried. Current fall detection monitoring systems use wearable or vision‐based sensors. The problem is research has shown that many seniors will not consistently wear a device on their body or allow cameras in their living space because of privacy concerns that severely limit the technology user acceptance. The current standard of care is self‐report or eyewitness and therefore fall events are often undetected and underreported due to human error. Hence, we are proposing a solution to address this unmet need with a novel fall management system engineered with radio frequency (RF) sensors that do not require an individual to wear or interact with a device. Method Six healthy participants simulated three types of falls, medial and lateral, anterior and posterior, and collapse in the Dementia and Elderly Care Robotics and Sensing (DECRS) Lab, University of Minnesota Duluth. The sensor was installed in the lab as it would be in the resident room of an LCTF. The simulated room included a bed, chair, and mattress on the floor to protect the participants. Result Our study found that the contactless fall detection and activity system identified falls at overall sensitivity rate of 89% with a specificity of 100% in 748 tests and activity was identified at 90%. Our system identified “medial and lateral” falls with a 92% sensitivity, “anterior and posterior” falls with a 91% sensitivity, and “collapse” falls with a 75% sensitivity. Conclusion Contactless sensing holds tremendous potential for monitoring individuals living with AD/ADRD and the elderly population for aging in place. LTCFs that are short staffed could benefit by utilizing tools that augment the capabilities of nurses with a continuous collection of patient‐generated health data (PGHD) and a notification system that is integrated with existing workflows and only send alerts for potential events that would require immediate attention.
Study on Effectiveness of Online Peer Review in C-E Translation

The author collects data from an online peer review platform (Peerceptive), where 25 junior English majors in Wenzhou Medical university were asked to exchange peer reviews of first drafts of 2 translation assignments and then make modifications on their own translation works based on peer reviews they received. The author analyzes the acceptance rate of peer reviews of 12 students, who are divided into 3 groups, good, average and weak based on their academic performance in their previous semester. The author also compares the test performance of the 12 students at the beginning of the semester and at the end of the semester. Finally, the author hands out questionnaires to collect students’ perceptions and attitudes towards online peer review in C-E translation. Based on a comprehensive and statistic analysis of the data collected from the 2 assignments, 1 test and 1 questionnaire, the author draws a conclusion that online peer review is beneficial for English major students in C-E translation.
Survey: Homomorphic Encryption-based Deep Learning that Preserves Privacy

When deep learning methods are effective, the volume of information accessible for training is expanding quickly, with large-scale user data collection in businesses taking the lead. Due to the sensitive nature of user data and the way. Data collection creates privacy concerns because of the potential for this material (images and audio recordings) to be preserved forever. The concepts of privacy and secrecy pertain to the avoidance of sharing this data, and a higher volume of data is required for deep learning to be achieved. When data must be accessed for training, machine-learning algorithms face certain difficulties. Several privacy-preserving deep learning solutions, such as multi-lateral computation secrecy and symmetric encryption within neural networks, have been developed to address these concerns
Mail Filing Application At Akper Rumkit TK. III Manado

In today's technological era, the important role of technology in supporting administrative activities in various agencies, including government and private institutions, cannot be ignored. One of the critical aspects of administration is document management, especially when it comes to archiving incoming and outgoing mail. Traditional document archiving by storing physical documents faces obstacles in large storage requirements, long search times, and the risk of damage due to attacks by intruding animals. This research focuses on developing a Mail Archiving System Application with a computerized approach at the Akper Rumkit Tk.III Manado Campus. The research method involves direct observation in the field, with the research object being administrative administration. The data collection process is done by searching for information from various sources, including books, journals, the internet, and interaction with the administrative administration. The results of this study created a computerized incoming and outgoing mail filing application. The application is designed to manage mail data efficiently and effectively, minimizing the constraints that often arise in traditional archiving. With this application, the mail filing process is expected to be more optimal, speed up the search for documents, and reduce the risk of damage that may arise. As an innovative solution, implementing this application is expected to positively contribute to improving administrative performance at the Akper Rumkit Tk.III Manado Campus.
Principles of modern mentoring in high school

The article is devoted to the analysis of the basic principles of mentoring in higher education. The authors revealed that the activities of the mentoring institute should be based on a number of basic principles, namely: scientificity, legality, variability, individualization, consistency, mutual respect, axiology, compe-tence and voluntariness. The study shows that the principle of a mentor’s work is to convey his own profes-sional experience - “do as I do.” In modern conditions, this is not enough, so mentoring should be considered from the perspective of meta-technology, where, within the framework of interaction, the mentor chooses the most suitable role to achieve the best effect (mentor, coach, tutor, facilitator). The conclusions are formulated that the activities of mentors need to be competently managed, planning work to “grow” personnel, identifying factors influencing the state of personnel potential, and forecasting its development based on the basic principles of organizing the activities of a mentor. The interest of higher education institutions should be traced in this, because Thanks to mentoring, new opportunities open up for them in the implementation of innovative and investment-attractive projects, which, in turn, is an indicator of interest in these educational institutions as platforms for recruiting promising personnel.
"Trado" Electronic Device for Learning Sign Language in Real Time Through Voice Commands

The absence of tools for learning signed language in children with loss of listening ability is the main problem that currently exists when interacting with society. The purpose of this work is to provide an alternative for learning signed language in real time with people who have complete or partial hearing disabilities. Help was taken from the Google Assistant to receive the voice of the tutor, these commands are processed by the microcomputer to translate it into text and images that show "Sign Language" (SL), so that the student can understand the message that has been given to him. issued by your teacher. Through the experimental design, each of the phases belonging to the process that was carried out for the device was developed, with the use of parameters in the configuration of the main components, the limitations for the use of the device were established. This device includes an instant, simple and easy way to convert written text received by means of a scanner camera placed at the end of a finger, decipher, or interpret it and translate it into a binary system which is sent to the six active buttons that They form the representations of the Braille system.
PENGEMBANGAN BAHAN AJAR MATA KULIAH PEMBELAJARAN BILINGUAL BERBASIS ESP BERORIENTASI PJBL UNTUK MAHASISWA PGSD UNIMED

This aim of this research is to develop teaching material of Billingual Learning Course for PGSD Student of Unimed. This research uses the dick and carrey model on develop the teaching material. The results of this research are: (1) Description of the needs for Bilingual learning materials, (2) Skills needed by students in communicating using English in teaching in elementary schools, (3) PJBL-based Bilingual Learning materials and (4) test results data/expert validation namely; English teaching experts gave a score of 36 with an average score for each questionnaire item = 3.6 and teaching materials experts gave a score of 39 with an average score for each questionnaire item = 3.9. Therefore, it can be concluded that the teaching materials of Bilingual Learning using the PJBL model have fulfilled the theoretical aspects of curriculum development and teaching materials development.
Validation of QyScore’s® fully automated quantitative image segmentation tools against expert manual gold‐standards

Abstract Background Quantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard. Method The validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression. Result Mean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®’s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2). Conclusion QyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response.
Synthetic data formation for machine learning recognition of underwater objects

The main problem of machine learning for control systems of unmanned underwater vehicles is objectively very small samples of real data. The study aim was to develop an approach to the creation of synthetic data describing underwater objects for as the samples for training and validation in machine learning of control systems for auton-omous unmanned underwater vehicles. The subject of the study was a variety of underwater objects because real information about the shape, size and their external images is very limited. The data augmentation method was used, which makes it possible to obtain additional data on underdetermined objects of observation from the initial data while maintaining the classification features. Eight models have been developed that imitate the influence of various factors of the aquatic environment and allow using various augmentation methods (changing the position, adding noise, glare to the image, defocusing to create fuzziness; fragmentation, etc.) to obtain an almost unlimited number of images of any objects of man-made activity immersed in underwater environment, to varying degrees similar to the reference. Examples of the use of augmentation models that take into account changes in illumina-tion, transparency and the presence of an underwater landscape are given. Such synthetic (model) images may be the basis of a training set for machine learning to recognize and identify underwater objects. The trained model can be used as the basis of a decision support system for operators of remote-controlled unmanned underwater vehicles and as the basis for building control systems for autonomous uninhabited underwater vehicles for moni-toring underwater spaces.