Reviewerly/exHarmony huggingface dataset:
Domain specific word embeddings for natural language processing in radiology

There has been increasing interest in machine learning based natural language processing (NLP) methods in radiology; however, models have often used word embeddings trained on general web corpora due to lack of a radiology-specific corpus.We examined the potential of Radiopaedia to serve as a general radiology corpus to produce radiology specific word embeddings that could be used to enhance performance on a NLP task on radiological text.Embeddings of dimension 50, 100, 200, and 300 were trained on articles collected from Radiopaedia using a GloVe algorithm and evaluated on analogy completion. A shallow neural network using input from either our trained embeddings or pre-trained Wikipedia 2014 + Gigaword 5 (WG) embeddings was used to label the Radiopaedia articles. Labeling performance was evaluated based on exact match accuracy and Hamming loss. The McNemar's test with continuity and the Benjamini-Hochberg correction and a 5×2 cross validation paired two-tailed t-test were used to assess statistical significance.For accuracy in the analogy task, 50-dimensional (50-D) Radiopaedia embeddings outperformed WG embeddings on tumor origin analogies (p < 0.05) and organ adjectives (p < 0.01) whereas WG embeddings tended to outperform on inflammation location and bone vs. muscle analogies (p < 0.01). The two embeddings had comparable performance on other subcategories. In the labeling task, the Radiopaedia-based model outperformed the WG based model at 50, 100, 200, and 300-D for exact match accuracy (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively) and Hamming loss (p < 0.001, p < 0.001, p < 0.01, and p < 0.05, respectively).We have developed a set of word embeddings from Radiopaedia and shown that they can preserve relevant medical semantics and augment performance on a radiology NLP task. Our results suggest that the cultivation of a radiology-specific corpus can benefit radiology NLP models in the future.
Methodology of building a master’s individual educational route for effective development of professional competencies

Abstract The 21 st century is characterized by the beginning of informatization and digital technologies. For a modern specialist to solve most problems, it is necessary to have a high level of professional competence and be receptive to innovation. The Bologna system provides for the opportunity to get a bachelor’s degree in one direction, and finish a master’s degree in another. Thus, the master’s program can be applied to both bachelors and specialists who have a diploma in related fields of knowledge (in the same enlarged group of specialties), or completely from other fields of knowledge. Some bachelors and specialists often do not have the necessary knowledge to master a new specialty, which makes it necessary to form an individual educational route. Today, in the system of higher professional pedagogical education, students are given the opportunity to choose the subjects for the variable part of the working curriculum in accordance with the educational program for the training profile. In addition, you can get additional professional education through retraining programs. In this case, the student himself becomes the subject of designing an individual educational route, who chooses a certain additional educational program.
A dual solar tracking system based on a light to frequency converter using a microcontroller

Previous studies have shown that sun tracking systems using single axis or dual axes tracking harvested more energy than fixed panels. Most of those studies were based on the light dependent resistors (LDR) with two or four LDRs depending whether single axis or dual axes is used. There are few concerns regarding those schemes. Because the LDRs are mounted on the same panel, they are continuously tracking the sun. As a result, their auxiliaries are drawing high currents; which means unnecessary consuming energy because of the continuous low speed rotation with many starts-stops; on top of this the heating problem due to high current consumption. Using LDRs only detects voltages not power whereas solar energy depends mainly on current where current is directly proportional to irradiation. As a result, and based on previous study, only about 85% of net energy is saved. They also suffer from the problem that each panel has got its own tracking. This study addresses those issues. The system is based on a PILOT-PANEL scheme, each equipped with a light to frequency converters (LTF). The advantage of LTF is that its output frequency is proportional to the irradiation which means proportional to power. The PILOT is small rod with its top in the W shape, where the first LTF is mounted on middle peak of the shape. The rod is driven by a micro-motor. The second LTF is mounted on the PANEL. The PILOT continuously tracks the sun. Every time the PILOT moves to a new position, a microcontroller reads both LTF's frequencies and compares them. If the difference is less than a pre-set value, the PANEL stays in the current position and the PILOT continues its tracking course. If the difference is bigger than the offset, the PANEL aligns itself with the PILOT. This process repeats itself during the course of the day. At the same time, another offline altitude angle tracking system comes into play. Because the range of the altitude angle in Bahrain is in the region of 35° between summer and winter, the microcontroller is programmed to move between four and eight time a year in steps of about 9°The technique has shown the extraction of 98.36% of continuous tracking with minimum energy consumption. This is certainly better than previous studies where the harvested energy is in the region of 85%. If continuous tracking represents 100%, at 1000 Hz (1.1 µW/cm2) offset, the extracted energy (as a percentage) was calculated to be 98.36%, at 4000 Hz (4.4 µW/cm2) offset, the energy was 95.41% and at 8000 Hz (8.8 µW/cm2) offset, the extracted energy was 79.9% and for fixed panel, it would be about 60%.
ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common aspect of daily life for many people. To improve the accessibility of these devices and to enable their usage in a variety of settings, building models that can assist users and accomplish tasks through the UI is vitally important. However, there are several challenges to achieve this. First, UI components of similar appearance can have different functionalities, making understanding their function more important than just analyzing their appearance. Second, domain-specific features like Document Object Model (DOM) in web pages and View Hierarchy (VH) in mobile applications provide important signals about the semantics of UI elements, but these features are not in a natural language format. Third, owing to a large diversity in UIs and absence of standard DOM or VH representations, building a UI understanding model with high coverage requires large amounts of training data. Inspired by the success of pre-training based approaches in NLP for tackling a variety of problems in a data-efficient way, we introduce a new pre-trained UI representation model called ActionBert. Our methodology is designed to leverage visual, linguistic and domain-specific features in user interaction traces to pre-train generic feature representations of UIs and their components. Our key intuition is that user actions, e.g., a sequence of clicks on different UI components, reveals important information about their functionality. We evaluate the proposed model on a wide variety of downstream tasks, ranging from icon classification to UI component retrieval based on its natural language description. Experiments show that the proposed ActionBert model outperforms multi-modal baselines across all downstream tasks by up to 15.5%.
A novel approach for global solar irradiation forecasting on tilted plane using Hybrid Evolutionary Neural Networks

An efficient management of solar power systems requires direct and continuous predictions of global irradiation received on inclined planes. This paper proposes a new approach that simultaneously estimates and forecasts inclined solar irradiation. The method is based on a multi-task Hybrid Evolutionary Neural Network with two output neurons: one estimates the irradiation at the current instant and another predicts it for the next hour. An Evolutionary Algorithm is used to learn the most proper topology (number of neurons and connections). Two studies are carried out to evaluate the performance of the method, considering experimental ground data for the same inclination angle and satellite data with different tilt angles. The data only contain one measured variable, what improves its applicability to other sites. The potential of three different basis functions in the hidden layer is compared (Sigmoidal Units, Radial Basis Functions and Product Units), concluding that the results achieved by Sigmoidal Units are better. Single and multi-task models are also compared with a statistical analysis, which shows no significant differences. However, the proposed multi-task option is much simpler and computationally efficient than individual models. The problem tackled is more complex and challenging than previous works, due to inclined solar irradiation is predicted based on the horizontal irradiation and also because the model simultaneously estimates and predicts irradiation. However, the performance obtained is excellent compared to the literature.
Autoregressive Reasoning over Chains of Facts with Transformers

This paper proposes an iterative inference algorithm for multi-hop explanation regeneration, that retrieves relevant factual evidence in the form of text snippets, given a natural language question and its answer.Combining multiple sources of evidence or facts for multi-hop reasoning becomes increasingly hard when the number of sources needed to make an inference grows.Our algorithm copes with this by decomposing the selection of facts from a corpus autoregressively, conditioning the next iteration on previously selected facts.This allows us to use a pairwise learning-to-rank loss.We validate our method on datasets of the TextGraphs 2019 and 2020 Shared Tasks for explanation regeneration.Existing work on this task either evaluates facts in isolation or artificially limits the possible chains of facts, thus limiting multi-hop inference.We demonstrate that our algorithm, when used with a pre-trained transformer model, outperforms the previous state-of-the-art in terms of precision, training time and inference efficiency.
Developing Interval-Based Cost-Sensitive Classifiers by Genetic Programming for Binary High-Dimensional Unbalanced Classification [Research Frontier]

Cost-sensitive learning is a popular approach to addressing the problem of class imbalance for many classification algorithms in machine learning. However, most cost-sensitive algorithms are dependent on manually designed cost matrices. Unfortunately, in many cases, it is often not easy for humans, even experts, to accurately specify misclassification costs for different mistakes due to the lack of domain knowledge related to actual situations in some complex unbalanced problems. As a result, these cost-sensitive algorithms cannot be directly applied. This paper proposes a new genetic programmingbased approach to developing cost-sensitive classifiers that are independent of manually designed cost matrices. The proposed method is able to construct classifiers and learn cost intervals automatically and simultaneously. In the proposed method, a tree representation, terminal sets and function sets are designed and developed. We examine the effectiveness of the proposed method on ten high-dimensional unbalanced datasets. The experimental results show that the proposed method often outperforms compared methods for highdimensional unbalanced classification. Furthermore, according to the analysis of evolved trees, the constructed classifiers often only need a small number of features to achieve a good classification performance.
Adaptation of Convolutional Neural Networks for Multi-Channel Artifact Detection in Chronically Recorded Local Field Potentials

Neural recording, known as local field potentials, offer valuable knowledge on how neural processes work and contribute to neural circuits. The recording can be contaminated by different internal and external sources of noise, because of the involvement of complex electronic apparatuses and the natural electrical activity throughout the body. In order to successfully utilize these signal, artifacts must be identified and removed. Thus, in this paper, an artifact detection method using a one-dimensional convolutional network referred to as 1D-CNN is proposed. The presented method achieved an improved accuracy and reduced computational time over the existing methods which use a multi-layered feed-forward neural network and a long-short term memory network. It also provided insight of the criteria behind the classification with gradient attribution maps.
A Six-Step Online Teaching Method Based on Protocol-Guided Learning during the COVID-19 Epidemic: A Case Study of the First Middle School Teaching Practice in Changyuan City, Henan Province, China

The first Middle School in Changyuan City, Henan Province has been relying on protocol-guided learning for a long time. During the COVID-19 epidemic, the school used protocol-guided learning as a carrier and conducted online teaching activities based on an online teaching platform. We have created a six-step teaching method for students to learn independently during the epidemic. We explored this school’s six-step teaching method based on protocol-guided learning during the COVID-19 epidemic.
Action Recognition From Thermal Videos Using Joint and Skeleton Information

Although various studies based on thermal images have been conducted, few studies have focused on the simultaneous extraction of joints and skeleton information of an object from a thermal image, and performed human action recognition using this information. Unlike in the case of visible light images, performing joint detection and skeleton generation on thermal images often leads to the complete disappearance of spatial information such as joints. In this case, it is extremely difficult to extract joints information from the object. Moreover, the accuracy of action recognition is significantly reduced owing to this issue. Therefore, a new method to extract joints and skeleton information is proposed in this study to address these issues. In the proposed method, an original 1-channel thermal image was converted into a 3-channel thermal image and then the images were combined to improve the extraction performance. A generative adversarial network (GAN) was used in the proposed method for extracting joints and skeleton information. In addition, research to recognize various human actions was conducted using the joints and skeleton information extracted by this method. The proposed human action recognition is performed by combining a convolutional neural network (CNN) and long short-term memory (LSTM). As a result of the experiments using self-collected and open data, it was found that the method proposed in this study shows good performance compared to other state-of-the-art methods.
A Survey Study on the Application of Modern Educational Technology in English Major College Teaching in the Age of 5G Communication

5G, the fifth generation of mobile communications technology, is the latest generation of cellular mobile communications technology, with high data rates, reduced latency, energy savings, reduced costs, increased system capacity and large-scale device connectivity. The Ministry of Education had made the integration of information technology and education and teaching development as a key task in 2018, and the arrival of the 5G era has also provided a greater development space for modern education technology, which has brought greater opportunities and challenges for education and teaching. Based on the existing research, this paper takes English major in a university in Northeast China as an example to investigate and analyze the application of modern educational technology in English majors in the 5G era, including the following aspects: 1) the comprehensive ability of English majors; 2) the application of modern educational technology in English major teaching; 3) the students' understanding of modern educational technology; 4) the advantages and disadvantages of the application of modern educational technology in the teaching of English majors; 5) the suggestions of English major students themselves about the application of modern educational technology in the major class learning. Then through the analysis of the survey results, the study thinks about how to make modern educational technology more effective, and combines with the traditional teaching means, puts forward suggestions for better application of modern education technology in English major teaching in the future, so as to promote the improvement of students' comprehensive quality and professional teaching effect.
Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers

The deep learning (DL)-based approaches in tumor pathology help to overcome the limitations of subjective visual examination from pathologists and improve diagnostic accuracy and objectivity. However, it is unclear how a DL system trained to discriminate normal/tumor tissues in a specific cancer could perform on other tumor types. Herein, we cross-validated the DL-based normal/tumor classifiers separately trained on the tissue slides of cancers from bladder, lung, colon and rectum, stomach, bile duct, and liver. Furthermore, we compared the differences between the classifiers trained on the frozen or formalin-fixed paraffin-embedded (FFPE) tissues. The Area under the curve (AUC) for the receiver operating characteristic (ROC) curve ranged from 0.982 to 0.999 when the tissues were analyzed by the classifiers trained on the same tissue preparation modalities and cancer types. However, the AUCs could drop to 0.476 and 0.439 when the classifiers trained for different tissue modalities and cancer types were applied. Overall, the optimal performance could be achieved only when the tissue slides were analyzed by the classifiers trained on the same preparation modalities and cancer types.
Mobile learning in chemical engineering: An outlook based on case studies

In the context of the electronic learning (E-learning) methodology, mobile learning (M-learning) focuses on the use of portable technology (such as mobiles or tablets) and the mobility of students. E-learning, and particularly M-learning, can be implemented in combination with other pedagogical methodologies for chemical engineering teaching and learning. We can consider that the vast majority of undergraduates own personal mobile devices nowadays. Moreover, many case studies have shown that M-learning is an effective methodology to capture students' attention and to actively engage them in the learning process. In most cases, lecturers have reported an improvement in both academic performance and qualifications and have expressed a favourable opinion towards this type of initiative in surveys. In line with the increasing interest in the incorporation of E-learning, this review discusses cases studies based on M-learning within the field of chemical engineering teaching through different technological platforms and apps which can be installed or directly used on mobile devices. All the platforms described in this work offer a free version, emphasizing the possibility of extending this methodology within the university with no need for additional economic resources.
Functional Self-Awareness and Metacontrol for Underwater Robot Autonomy

Autonomous systems are expected to maintain a dependable operation without human intervention. They are intended to fulfill the mission for which they were deployed, properly handling the disturbances that may affect them. Underwater robots, such as the UX-1 mine explorer developed in the UNEXMIN project, are paradigmatic examples of this need. Underwater robots are affected by both external and internal disturbances that hamper their capability for autonomous operation. Long-term autonomy requires not only the capability of perceiving and properly acting in open environments but also a sufficient degree of robustness and resilience so as to maintain and recover the operational functionality of the system when disturbed by unexpected events. In this article, we analyze the operational conditions for autonomous underwater robots with a special emphasis on the UX-1 miner explorer. We then describe a knowledge-based self-awareness and metacontrol subsystem that enables the autonomous reconfiguration of the robot subsystems to keep mission-oriented capability. This resilience augmenting solution is based on the deep modeling of the functional architecture of the autonomous robot in combination with ontological reasoning to allow self-diagnosis and reconfiguration during operation. This mechanism can transparently use robot functional redundancy to ensure mission satisfaction, even in the presence of faults.
Effective Meta-Attention Dehazing Networks for Vision-Based Outdoor Industrial Systems

Haze seriously affects the reliability of industrial systems, especially vision-based outdoor industrial systems such as autopilot systems. A majority of existing dehazing methods are not specifically designed for industrial systems and do not consider the reliability and resource cost of industrial system implementation. In this article, a novel meta-attention dehazing network (MADN) is proposed for direct restoration of clear images from hazy images without using the physical scattering model. Combined with parallel operation and enhancement modules, the meta-network automatically selects the most suitable dehazing network structure based on the current input hazy image by a meta-attention module. In addition, a novel feature loss calculated by the meta-network is proposed, which can accelerate the convergence of the dehazing network to meet the application requirements of practical industrial systems. A large number of experimental results on synthetic and real-world datasets show that the proposed MADN satisfies the needs of industrial systems.