Reviewerly/exHarmony huggingface dataset:
Semi-Automatic Knowledge Extraction from COVID-19 Scientific Literature: the COKE Project

Abstract Background The COVID-19 pandemic highlighted the importance of rapidly updating scientific information. However, the guidelines’ drafting process is highly time- and resource-consuming. The COKE Project aims to accelerate and streamline the extraction and synthesis of scientific evidence. To do so, the Project used deep learning to implement a semi-automated system that enhances the systematic literature review processes. We aim to show some preliminary results on the automatic classification of abstract sentences in papers related to COVID-19. Methods The tool is based on Natural Language Processing algorithms to detect and classify PICO elements and medical terms and organize abstracts accordingly. We built a BERT + bi-LSTM language model. The tool was trained on a corpus of 24,668 abstracts unrelated to COVID-19. We assessed the tool performance in a specific topic related to COVID-19 that has not been covered during training. To carry out manual validation, we randomly selected 50 abstracts. Abstract sentences were classified by 2 domain experts into 7 types: Aim (A), Participants (P), Intervention (I), Outcome (O), Method (M), Results (R), and Conclusion (C). The performance of the tool was compared with that of the experts in terms of precision, recall, and F1. Results The classifier proved to have a 76% overall accuracy. Precision, recall, and F1 were above 75% for all types of sentences except I, M, and P. Conclusions The results indicate a promising ability of the semi-automated classifier to predict expert-validated labels on abstracts of different topics. Our proposed tool is expected to significantly reduce the effort for producing medical guidelines and therefore have a strong, positive impact, particularly in emergency scenarios. The COKE Project also represents a call-to-action for similar initiatives, aimed at enhancing the information extraction process in medicine. Key messages • A rapidly changing healthcare requires fast decisions supported by scientific evidence. This is not compatible with the human limits in cognitive skills that reduce the ability to extract information. • The COKE Project aims to speed up the creation of healthcare guidelines, semi-automating parts of the workflow, and supporting the human-performed process of extracting and analyzing contents.
Abnormal Action Detection of Older Adults Using Graph Convolution Autoencoder

The increase in the elderly population living alone is considered the most serious problem. The elderly people living alone are difficult to cope with emergencies situations properly, leading to lonely deaths. Although various monitoring systems for emergency situations at home have been developed, some limitations still exist in identifying abnormal behaviors of the elderly people because of frequent equipment malfunctions. To address this situation, we use ETRI-Activity3D recorded in locations that mimic actual living spaces focused on the daily activities of the elderly people. In this study, we propose a methodology to detect abnormal actions of elderly people using a graph convolutional autoencoder. By capturing the spatial temporal feature of each action through graph convolution operation, the proposed method outperforms other existing methods in detecting abnormal actions. We hope that our work can help elderly people in real emergency situations.
ROS-based laser SLAM for autonomous navigation of smart carts

At present, traditional intelligent carts have positioning system (GPS) and obstacle avoidance system (ultrasonic, infrared), but due to the limitation of accuracy or the influence of the environment, the above-mentioned solutions always have inaccurate identification under the unknown area and error in the location of the positioning area when performing tasks outdoors, and at the same time, the navigation and obstacle avoidance functions are not perfect, this paper designs an intelligent ROS and laser SLAM-based trolley. Compared with the traditional intelligent cart, ROS intelligent cart, through the ROS in Raspberry Pi for master control, STM32 as slave control, through the laser SLAM algorithm, instant positioning, scanning obstacles, planning the optimal route, to achieve autonomous positioning and navigation, the real sense of self-driving. In this paper, the LIDAR SLAM builder is realized and the ROS cart is able to navigate autonomously.
The Ink Stroke Density and Width Characteristics of Ballpoint Pen in Forensic Documental Analysis

The purpose of this study was to confirm the possibility of using the ink stroke density and width as a non-destructive analysis factor for estimating the ink and ball size of a ballpoint pen in the field of identification of writing instruments for document evaluation. The density and width of strokes were measured by applying a constant pressure (100-300 g) to a total of 39 ballpoint pens of 4 types of ink (oil, low-viscosity oil, gel and water) and ball size (0.5-1.0 mm). As a morphological characteristic of stroke density, it was confirmed that there were four types: 1) Selective diffusion of high-viscosity ink, 2) Agglomeration, 3) Dot, 4) Selective diffusion of low-viscosity ink. The deposition value of ink stroke density showed a tendency to be measured twice as low for oil (33.74~47.26%) with high viscosity compared to gel (78.19~84.66%) and water (44.96~85.26%) with low viscosity of ink. The stroke width showed a tendency to increase as the pressure applied to the ballpoint pen and the size of the ball increased. The stroke width according to the ink was similar in the case of oily and low-viscosity oils, but in the case of neutral, it was about 100 μm higher than that of oil and low-viscosity oils. As a result, the density and width of strokes showed a difference in the characteristics that appeared according to the viscosity of the ink. In the order of non-destructive analysis for the identification of writing instruments, it is necessary to ① analyze the deposit shape and deposit value of ink stroke density, and ② to estimate the ink and ball size through ink width measurement. It was confirmed that it can be used.
School visits to a physics research laboratory using virtual reality

School visits to research laboratories or facilities represent a unique way to bring students closer to science and STEM (Science, Technology, Engineering and Mathematics) careers. However, such visits can be very expensive for students and teachers, in terms of both time and money. In this paper, we present a possible alternative to on-site visits consisting inan activity addressed to high school students that makes use of a VR application to make them enter into a particle physics experiment. This proposal can represent a valid way of guaranteeing a visit to a research centre for all schools, regardless of their social or geographical origin. We describe the tests we carried out with a focus group of teachers and their students, and the obtained results.
SEGMENTATION-BASED CLASSIFICATION OF HUMAN BLASTOCYST USING DEEP LEARNING

This work aimed to build an automatic system with Geri® time-lapse images for evaluating blastocyst morphology based on deep learning algorithms. Also, we studied to what extent the models emulate the skill of a trained specialist through the influence of segmentation of blastocyst structures.
Gas Engineering

Volume 2 covers the constituents of gas streams and their properties. The author presents the chemistry and engineering aspects of the methods and principles by which the gas streams might be cleaned from their noxious constituents. The concept of gas condensate is also discussed as well as the methods which can be applied to the analysis of streams and condensate. Vol. 1: Origin and Reservoir Engineering. Vol. 3: Uses of Gas and Effects.
Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts.
Trajectory Planning of Aerial Robotic Manipulator Using Hybrid Particle Swarm Optimization

The trajectory planning of an aerial robotic manipulator system is studied using Hybrid Particle Swarm Optimization (HPSO). The aerial robotic manipulator is composed of an unmanned aerial vehicle (UAV) base and a robotic manipulator. The robotic manipulator is dynamically singular. In addition, strong coupling exists between the UAV base and the robotic manipulator. To overcome the problems, the trajectory planning is studied in the join space using HPSO. HPSO combines superiorities of PSO and GA (Genetic Algorithm), prohibiting particles from becoming trapped in a local minimum. In addition, the control parameters are self-adaptive and contribute to fast searching for the global optimum. The trajectory planning problem is converted into a parameter optimization problem. Each joint trajectory is parameterized with a Bézier curve. The HPSO is implemented to optimize joint trajectories, satisfying specific objectives and imposed constraints. Numerical simulations are also carried out to validate the effectiveness of the proposed method.
Human instance guided two-stream network for person reidentification

Since the person reidentification (Re-ID) technique was proposed, it has successively produced successful research. The performance of this technique still has room for improvement due to the challenges of cluttered background and spatial misalignment. Recently, researchers have attempted to integrate human parsing or pose estimation results to capture person regions for mitigating these problems. However, many important and recognizable cues remain in the background regions. To mine these useful cues while still noticing the informative human body parts, this work proposes a simple yet effective two-stream model that utilizes one stream from human instance to learn discriminative human part features and enriches the representation with the other stream from the original image. Moreover, to alleviate the spatial misalignment problem, this paper rectifies the feature extraction regions with the assistance of human segmentation results. Experimental results demonstrate that the proposed method significantly improves the performance of person Re-ID.
Developing Comprehensive Predictive and Prescriptive Management System for Flexible Pipeline Survivability

Abstract In 2020, PETRONAS had lost a flexible pipeline due to an increase level of contaminants in the process stream, which carries into the flexible pipeline system. The incident had caused a major cost impact in which the pipeline needs to be replaced and the production had to be differed for at least 1-year. Detail investigation was conducted, and the finding indicates that amongst the root cause was the unavailability of proper predictive and prescriptive approach in managing flexible pipeline integrity system. Thus, this had triggered PETRONAS to embark an initiative to develop a Flexible Pipeline Management System, which then will be included and further developed alongside with PETRONAS digitalization journey. PETRONAS Flexible Pipeline Management System will be developed by upgrading the existing PETRONAS Digital Management System for carbon steel pipeline. As an overview, scopes for the Flexible Pipeline Management System are comprises of guidelines development, risk parameters establishment, fitness for service (FFS) assessment method advancement, and flexible pipeline remaining life extension study (FLES). Its development started with leveraging on PETRONAS experience in carried out integrity assessment for the existing flexible pipeline with expired design life using analytical method. The assessment was conducted in collaboration with flexible pipeline integrity experts which had been included as part of PETRONAS capability up skilling program. Progressively, the flexible pipeline integrity management guideline, risk assessment protocol, and predictive assessment method for FFS and PLES were developed and corelated within. The above elements had been grouped and formed as Flexible Pipeline's integrity assessment manual. It has then been piloted to one of PETRONAS existing flexible pipeline and turned out very successful. The pilot had resulted in eliminating the need of pipeline replacement and generated cost avoidance of more than USD10 Mil - by assessing the remaining life of the pipeline, managing the risk failures through inspection and monitoring plan, as well as minor repair(s) whenever specified.
The rise of phishing: what your staff needs to know

Phishing attacks have surged in waves over the past 18 months, capitalising on the fear and uncertainty caused by the Covid-19 pandemic. Yet these media-publicised waves overshadow the fact that phishing attacks, year over year, have continued their inevitable climb to reach peak numbers through pandemic-related topics
Pashtu Language Digits Dataset

Pashtu is a language spoken by 50 million people in the world [1]. It is the national language of Afghanistan and also spoken in the two largest provinces of Pakistan. It is a language written in complex way by calligraphers. Instead of enormous literature and research work in Optical Character Recognition for other languages of the world, this language still requires a mature optical character recognition system [2], [3]. A real dataset of Pashtu digits having 50000 scanned images is introduced and made publically available in this paper. All the digits in the images are handwritten images written and collected from faculty members, staff, and students of the Pak-Austria Fachhochschule, Institute of Applied Sciences and Technology, Pakistan. A total of 1250 candidates appeared in writing the text, out of which half are male and half female. The dataset will be publically available for research purposes.
Novel Data Analysis Framework in Smart Healthcare

Managing and processing the massive amount of heterogeneous data collected from IoT is a difficult task, and today’s infrastructure resources have been confronted as a result. Moreover, it becomes difficult to provide the user with significant information to make a decision due to the existence of massive information records. It is now possible to extract critical insights from massive streams of information in real time using complex event-processing (CEP) technology. Here an attempt is made to design a system that can generate complex events from large sensor data and send only those events to the cloud server. For generating complex events, the sensor’s threshold values are taken into consideration. If the sensor reading surpasses the threshold boundary, an event is formed and the associated data is forwarded to the cloud (in our case, we are using a temperature sensor, a pulse oximeter, and an ECG sensor). It avoids sending unnecessary data on the cloud and, subsequently, reduces data transfer time and memory space. Esper library is used for the CEP approach. The Viterbi algorithm and the support vector machine (SVM) are used to estimate the patient’s condition by delivering an emergency notice is to the physicians.
LETCP: A Label-Efficient Transformer-Based Contrastive Pre-Training Method for Brain Tumor Segmentation

The manual annotation of brain tumor images is costly and relies heavily on physician expertise, which limits the implementation of automated and accurate brain tumor segmentation in clinical practice. Meanwhile, unlabeled images are readily available but not well-exploited. In this paper, a novel brain tumor segmentation method for improving the efficiency of labeled images is proposed, dubbed LETCP. Specifically, it presents a contrastive pre-training strategy that utilises unlabeled data for model pre-training. The segmentation model in this approach is constructed based on a self-attention transformer. Extensive evaluation of the method is performed on three public datasets. By using pre-training with unlabeled data and then fine-tuning with small amounts of labeled data, this method achieves segmentation performance surpassing other semi-supervised methods and shows competitive performance compared with supervised methods.
Tuturan Perintah Guru dalam Pembelajaran Bahasa Indonesia dengan Metode Ceramah Plus Tanya Jawab dan Tugas (CPTT) di SD Negeri 01 Maguan

The teacher's orders are verbal tactics used to condition the learning process and encourage students to do something. This study aims to determine the types and functions of teacher orders in class IV in Indonesian language subjects at SD Negeri 01 Maguan, Malang Regency and describe the application, advantages, and disadvantages of the lecture method plus questions and answers and assignments (CPTT) used by teachers. The study used a qualitative descriptive design with an ethnographic communication approach. The source of the research data is the speech of the fourth-grade Indonesian teacher at SDN 01 Maguan, Malang Regency. Data were collected by using observation, recording, and recording techniques. The data analysis technique was carried out by recording all the speeches of teachers and students in class IV SDN 01 Maguan, analyzing, and classifying the speech data of teacher orders related to the lecture method plus questions and answers and assignments. The results showed that there were 4 types of command utterances in the form of imperative sentences of orders, requests, appeals, and invitations. The CPTT method is used to provide some knowledge to students so that the learning process can take place effectively and efficiently. The use of the CPTT method shows its advantages, namely that teachers can directly measure the level of understanding of students through the question-and-answer process and assignments. And there is a weakness that students become passive because learning is centered on the teacher's lecture
Glossary of Terms and Concepts in Medical Padiation Physics. Terminology Used in Technological Support of Radiation Therapy

Medical radiation physics supports medical diagnostic and treatment process based on the use of ionizing radiation in radiation therapy, nuclear medicine, radiation diagnostics and radiation safety of patients and personnel. However, the successful implementation of this process requires close interaction&#x0D; of specialists in all the above areas. The basis of such interaction is the unification of the terminology&#x0D; and concepts used at various stages of the diagnostic and treatment process. The article presents a&#x0D; glossary on technological support of radiation therapy which contains the most commonly used terms&#x0D; in this field and explanations for each of them. The glossary is intended for use both in professional education, including postgraduate education, and for medical physicists and radiation oncologists working in radiological medical organization
Nutritional Status Classification of Toddlers Using K-Nearest Neighbor Algorithm

Nutrition is important for the balance of the human body. Knowing the nutritional status is very important to realize the good and the quality human resources. Posyandu Parkit is one of the many Integrated Healthcare Center (posyandu) in Indonesia that provides health services for the community, one of which is monitoring and nutritional development of toddlers. However, the Posyandu Parkit in determining the nutritional status of toddlers is done manually; this method uses measurement parameters based on body weight (BB/U) which are less specific in showing the nutritional status of the toddler by matching manually with a reference standard table available in a healthy card (KMS). The purpose of this research is to produce a website that can determine the nutritional status of toddlers quickly and accurately. The system is designed using the K-Nearest Neighbor method which is a classification method. The K-Nearest Neighbor process is carried out by calculating the distance between the test data and the training data using the Euclidean distance formula, before sorting from the closest distance to the k-th order, then nutritional status is determined. The results of this study are a website that can determine the nutritional status of toddlers by applying the K-Nearest Neighbor algorithm with BB/U, TB/U, and BB/TB accuracies were 93.75%, 87.5%, and 93.75 %, respectively.
Model Complexity of Deep Residual U-NET for CT Liver Volumetry

Computed Tomography (CT) has been used for liver volume measurement because of the highest location accuracy. Automated segmentation methods may improve CT volumetry time, but it has low accuracy. Residual U-Net which is one of the deep learning methods could improve segmentation accuracy. However optimization of residual U-Net hasn’t been demonstrated yet. The purpose of this paper is to investigate the optimal complexity for CT liver volumetry. The study was conducted using the 3D-IRCADb01 Datasets (10 males, 10 females) published by MIS Training Center, 15 people learned and 5 people tested. Segmented images were generated using Deep Residual U-Nets with a total of four different complexity. As a result, as the model became more complex, the total parameters and training time increased exponentially. In all models, both training and testing showed more than 97% accuracy. All losses were less than 0.2. In the case of DCL, it was the lowest at 0.8037 in 3-layer and the highest at 0.9533 in 5-layer. In conclusion, 5 hidden layers of residual U-Net has the highest dice coefficient loss and could train the datasets faster than other complex models.
3D Skeletal Volume Templates for Deep Learning-Based Activity Recognition

Due to advances in depth sensor technologies, the use of these sensors has positively impacted studies of human-computer interaction and activity recognition. This study proposes a novel 3D action template generated from depth sequence data and two methods to classify single-person activities using this 3D template. Initially, joint skeleton-based three-dimensional volumetric templates are constructed from depth information. In the first method, images are obtained from various view angles of these three-dimensional templates and used for deep feature extraction using a pre-trained convolutional neural network. In our experiments, a pre-trained AlexNet model trained with the ImageNet dataset is used as a feature extractor. Activities are classified by combining deep features and Histogram of Oriented Gradient (HOG) features. The second approach proposes a three-dimensional convolutional neural network that uses volumetric templates as input for activity classification. Proposed methods have been tested with two publicly available datasets. Experiments provided promising results compared with the other studies presented in the literature.