Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Quadcopter Based on a Linear Quadratic Regulator (LQR)

This paper presents the design and construction and control of a quadcopter drone for Aerial Data Collection (ADC). The frame of the drone was designed using CadDian Software and the parts were printed using a 3D printer. The flight controller was based on Arduino board using an Atmega328p microprocessor with GSM, GPS and GPRS for sending data over the internet and also enhancing long range flight. A feedback control system was developed and tested to control the stability of drone. The proposed control strategy of the drone was tested for a case of pursuit of trajectory and also for speed of response and the findings were very positive confirming the appropriateness of the control measures for independent and autonomous flying with promising precision. This Unmanned Aerial Vehicle (UAV) fitted with IoT has the capability of collecting and sending data over the internet and therefore can be used in many applications including risk assessment, forestry management, urban planning, coastal zone management, infrastructure monitoring, post-disaster damage assessment and delivery of medical supplies.
Deep Learning Based Approach For Malaria Detection in Blood Cell Images

Malaria, a life-threatening disease, develops due to the bite of female Anopheles mosquito. It spreads the plasmodium parasites in human blood, killing hundreds of millions of people every year. Modern scientific advancements play a pivotal role to combat the disease, along with biomedical research by the medical experts to possibly eradicate this disease from all parts of the world. With the significant development in deep learning research, faultless identification of medical imaging has become an important factor in medical diagnosis and decision-making. To this end, we present a deep learning based approach using a convolutional neural network for detecting malaria from microscopic cell images using image classification. The proposed CNN model implemented using 5-fold cross validation approach outperforms all the existing methods in terms of accuracy and other evaluation metrics, thus achieving the best results till date in malaria detection using deep learning.
3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles

In this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value.We describe the system entered by the team of Information Retrieval Lab.Universitas Indonesia (3218IR) in the SemEval 2020 Task 11 Sub Task 1 about propaganda span identification in news articles.The best model obtained an F1 score of 0.24 in the development set and 0.23 in the test set.We show that there is a potential for performance improvement through the use of models with appropriate hyper-parameters.Our system uses a combination of Conv1D and GloVe as Word Embedding to detect propaganda in the fragment text level.
Application of mixed reality technique for the surgery of oral and maxillofacial tumors

Objective To explore the application of mixed reality technique for the surgery of oral and maxillofacial tumors. Methods In this study, patients with a diagnosis of an oral and maxillofacial tumor who were referred to Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology from December 2018 to January 2020 were selected. The preoperative contrast-enhanced computed tomography data of the patients were imported into StarAtlas Holographic Medical Imaging System (Visual 3D Corp., Beijing, China). Three-dimensional (3D) model of tumor and key structures, such as skeleton and vessels were reconstructed to three-dimensionally present the spatial relationship between them, followed with the key structures delineation and preoperative virtual surgical planning. By using mixed reality technique, the real-time 3D model was displayed stereotactically in the surgical site. While keeping sterile during operation, the surgeon could use simple gestures to adjust the 3D model, and observed the location, range, and size of tumor and the key structures adjacent to the tumor. Mixed reality technique was used to assist the operation: 3D model registration was performed for guidance before tumor excision; intraoperative real-time verification was performed during tumor exposure and after excision of the tumor. The Likert scale was used to evaluate the application of mixed reality technique after the operation. Results Eight patients underwent mixed reality assisted tumor resection, and all of them successfully completed the operation. The average time of the 3D model registration was 12.0 minutes. In all the cases, the surgeon could intuitively and three-dimensionally observe the 3D model of the tumor and the surrounding anatomical structures, and could adjust the model during the operation. The results of the Likert scale showed that mixed reality technique got high scores in terms of perceptual accuracy, helping to locate the anatomical parts, the role of model guidance during surgery, and the potential for improving surgical safety (4.22, 4.19, 4.16, and 4.28 points respectively). Eight patients healed well without perioperative complications. Conclusion By providing real-time stereotactic visualization of anatomy of surgical site and guiding the operation process through 3D model, mixed reality technique could improve the accuracy and safety of the excision of oral and maxillofacial tumors.
Opportunity for Expression: COVID-19, Online Assignments, and Expressive Arts

“My first instinct was to jump into originally planned content when the semester resumed. I already had the assignments ready, and it would have been easy for me to move everything back one week and expect students to proceed as scheduled. But, a feeling stopped me. I was having a difficult time emotionally with plans changing and events canceling. I was exploring what type of educator I could be during virtual learning. If I was experiencing anxiety, my students undoubtedly were as well. Could I really expect them to shut down these feelings and plunge into the curriculum I created before the pandemic? With this in mind, I could not begin the rest of the semester as planned. I sought to create a virtual space where students wanted to participate. This plan meant reevaluating my curriculum.”
Pancreas Segmentation in Abdominal CT Images with U-Net Model

Pancreas is one of the most challenging organs in segmentation due to its different shape, position and size in each human being. With the development of machine learning, various deep learning methods are applied to segment the pancreas among organs in the abdominal region. In this study, pancreas segmentation is performed using the U-Net model, which is one of the convolutional neural networks (CNN) models. The results of pancreas segmentation performed on the Pancreas CT data set obtained from The Cancer Imaging Archive (TCIA) database containing computed tomography images of 82 patients are presented in detail. According to the results, Dice similarity coefficient and Jaccard similarity coefficient are found to be 0.78 and 0.66, respectively.
Discovery of Gold Nanoparticles in Marcellus Shale

A high density of gold (Au) nanoparticles has been observed on the surfaces of the coexisting opal nanospheres in kerogen-bearing shales from the Marcellus Formation. Our analyses of the Au nanoparticles and associated minerals indicate that this represents a new formation type of colloidal gold nanoparticles. Additionally, the opal nanospheres are a new kind of natural opal-A that is characteristic of the mesoporous texture formed through oil-in-water emulsion processes. High-resolution transmission electron microscopy (TEM) combined with energy-dispersive X-ray spectroscopy analysis directly reveals spherical gold nanoparticles and gold nanorods associated with mesoporous opal nanospheres. Our newly found textures indicate that opal nanospheres may have facilitated the co-precipitation of pure gold nanoparticles in organic-bearing, reduced environments. Furthermore, our TEM observations provide distinct images of the initial phase of colloidal gold and silica, which may contribute to the formation of Au deposits involving “invisible” gold, secondary supergene enrichments, or high-grade gold accumulations. The discovery of Au nanoparticles in hydrocarbon-rich shale formations such as Marcellus (which contains 0.6–4.1 wt % gold/opal) suggests a potential for co-production of both gas/oil and gold—two valuable commodities.
The NiuTrans Machine Translation Systems for WMT20.

This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks. We participated in Japanese English, English->Chinese, Inuktitut->English and Tamil->English total five tasks and rank first in Japanese English both sides. We mainly utilized iterative back-translation, different depth and widen model architectures, iterative knowledge distillation and iterative fine-tuning. And we find that adequately widened and deepened the model simultaneously, the performance will significantly improve. Also, iterative fine-tuning strategy we implemented is effective during adapting domain. For Inuktitut->English and Tamil->English tasks, we built multilingual models separately and employed pretraining word embedding to obtain better performance.
Problems of professional education in Russia: quality monitoring of educational programs

The introduction of complex management systems in educational organizations is very expensive, often without taking into account the specifics of the organization and causing the need to install and adapt additional software and hardware. Such utilities, as a rule, are cheaper and consider the possibility of step-by-step input of separate subsystems and their modification, providing means for considering the available experience of creation and operation of separate subsystems. All this is aimed at supporting the educational process but does not take into account the assessment of the preparation quality for the implementation of the educational process itself. The main element of such preparation is the formation of educational programs.
Pseudo-Supervised Learning for Semantic Multi-Style Transfer

Numerous methods for style transfer have been developed using unsupervised learning and gained impressive results. However, optimal style transfer cannot be conducted from a global fashion in certain style domains, mainly when a single target-style domain contains semantic objects that have their own distinct and unique styles, e.g., those objects in the anime-style domain. Previous methods are incongruent because the unsupervised learning can not provide the semantic mappings between the multi-style objects according to their unique styles. Thus, in this paper, we propose a pseudo-supervised learning framework for the semantic multi-style transfer (SMST), which consists of (i) a pseudo ground truth (pGT) generation phase and (ii) a SMST learning phase. In the pGT generation phase, multiple semantic objects of the photo images are separately transferred to the target-domain object styles in an object-oriented fashion. Then the transferred objects are composed back to an image, which is the pGT. In the SMST learning phase, a SMST network (SMSTnet) is trained with the pairs of the photo images and its respective pGT in a supervised manner. From this, our framework can provide the semantic mappings of multi-style objects. Moreover, to embrace the multi-styles of various objects into a single generator, we design the SMSTnet with channel attentions in conjunction with a discriminator dedicated to our pseudo-supervised learning. Our method has been applied and intensively tested for anime-style transfer learning. The experimental results demonstrate the effectiveness of our method and show its superiority compared to the state-of-the-art methods.
A Continuation Semantics for Abstract Meaning Representation

Abstract Meaning Representation (AMR) is a simple, expressive semantic framework whose emphasis on predicate-argument structure is effective for many tasks. Nevertheless, AMR lacks a systematic treatment of projection phenomena, making its translation into logical form problematic. We present a translation function from AMR to first order logic using continuation semantics, which allows us to capture the semantic context of an expression in the form of an argument. This is a natural extension of AMR’s original design principles, allowing us to easily model basic projection phenomena such as quantification and negation as well as complex phenomena such as bound variables and donkey anaphora.
School Feeding Programs in Brazil: A Case Study of Vegetables Distribution using Social Network Analysis in Mogi das Cruzes City

(2021). School Feeding Programs in Brazil: A Case Study of Vegetables Distribution using Social Network Analysis in Mogi das Cruzes City. Journal of International Food & Agribusiness Marketing. Ahead of Print.
Disentangling Representations of Text by Masking Transformers

Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a range of downstream tasks.In this paper we explore whether it is possible to learn disentangled representations by identifying existing subnetworks within pretrained models that encode distinct, complementary aspects.Concretely, we learn binary masks over transformer weights or hidden units to uncover subsets of features that correlate with a specific factor of variation; this eliminates the need to train a disentangled model from scratch for a particular task.We evaluate this method with respect to its ability to disentangle representations of sentiment from genre in movie reviews, toxicity from dialect in Tweets, and syntax from semantics.By combining masking with magnitude pruning we find that we can identify sparse subnetworks within BERT that strongly encode particular aspects (e.g., semantics) while only weakly encoding others (e.g., syntax).Moreover, despite only learning masks, disentanglement-via-masking performs as well as -and often better thanpreviously proposed methods based on variational autoencoders and adversarial training.
THE EFFECT OF VIRTUAL REALITY ENHANCED LEARNING ENVIRONMENT ON THE 7TH-GRADE STUDENTS' READING AND WRITING SKILLS IN ENGLISH

The aim of this study is to investigate the effect of virtual reality (VR) enhanced learning environment on the 7th-grade students’ academic achievements in reading and writing in English. Within the scope of the experimental application of the research, the “planets” unit topic was selected from the 7th-grade syllabus of the 2018 Turkish English Language Teaching (ELT) curriculum for primary and secondary schools and a VR application about planets was developed for the research. In this study, a quasi-experimental research design was used. The achievement test, which was developed by the researchers before the experiment, was applied to both 15 experimental group and 11 control group students, who were volunteers and who were selected randomly in the seventh grade of a public secondary school in Izmir, Turkey. Before the study, the students in the experimental group were informed how to use VR headset and components. After a total of 3 weeks of implementation, the achievement test was applied to the students in the experimental and control groups. It is found that the experimental implementation has a positive effect on the academic achievement of the experimental group students regarding the reading and writing in English. In addition, the findings reveal that immersive VR is superior to frontal teaching by teachers and all other classical materials especially textbooks by delivering students sensory information in three dimensions. This study is based on quantitative research. For that reason, further studies based on qualitative research design can provide extra insights to get more educational evidence on the use of VR-Applications in English teaching.
The Use of Smartphone Applications in Laboratory Activities in Developing Scientific Communication Skills of Students

Smartphone applications have been widely used in everyday life, not least in the world of education. The use of smartphone applications in improving the quality of learning has become a trend and is widely used by educators. The use of smartphones in learning is dominated by the implementation of regular learning and is still lacking in the laboratory's implementation. This study aimed to determine the use of smartphone applications in laboratory activities in developing students' scientific communication skills. The research method used was the quantitative descriptive method. The research sample was a fifth-semester physics education study program student who contracted the school physics laboratory course. The smartphone application used in laboratory activities in this study consists of a physics toolbox and tracker. Physics toolbox is an application whose working principle is based on sensors. Students use the tracker to make graphs from videos that illustrate activities laboratory. The research instrument used was a performance assessment in the form of a scientific communication skill observation sheet. The data analysis technique was done by calculating the percentage of achievement of students' scientific communication skills. The results showed that smartphone applications could develop students' scientific communication skills in a good category. Thus, smartphone applications in laboratory activities can develop one of the skills needed by students in facing the era of industrial revolution 4.0.
Cyberbullying Detection Through Machine Learning: Can Technology Help to Prevent Internet Bullying?

Cyberbullying constitutes a threat to adolescents’ psychosocial wellbeing that developed alongside technological progress. Detecting online bullying cases is still an issue because most of victims and bystanders do not timely report cyberbullying episodes to adults. Therefore, automatized technologies may play a critical role in detecting cyberbullying through the use of Machine Learning (ML). ML covers a broad range of techniques that enables systems to quickly access and learn from data, and to make decisions about complex problems. This contribution aims at deepening the role of ML in cyberbullying detection and prevention. Specifically, the following issues are addressed: i. identifying the features most frequently considered to develop ML models predicting cyberbullying; ii. identifying the most used ML algorithms and their evaluation methods; iii. understanding the implication of ML for prevention; iv. highlighting the main theoretical and methodological issues of ML algorithms in predicting cyberbullying. To answer these research questions, a systematic review of literature reviews, from a total of n=186 records from online databanks, has been conducted. Ten literature reviews have been elected to analyze and discuss evidence about ML preventative potential against cyberbullying. Most of the models used content-based features to predict cyberbullying. The majority of these features includes words written in social network posts, whereas Support Vector Machine, Naïve Bayes, and Convolutional Neural Networks are the most used alghorithms. Methodological and technical issues have been critically discussed. ML represents an innovative preventative strategy that may optimize and integrate educational programs for adolescents and be the starting point of the development of technology-based automatized detection strategies. Future research is challenged to develop algorithms capable of detecting cyberbullying from several multimedia sources.
Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.
GPCNN: Evolving Convolutional Neural Networks using Genetic Programming

Image classification is an important task that has a wide range of applications. Convolutional neural networks (CNNs) are a common approach that can achieve promising performance in image classification. However, using CNNs to address a problem requires in-depth knowledge about CNN architectures and how it relates to the problem domain. Genetic programming (GP) as an evolutionary computation method can used to reduce the amount of knowledge required to design a CNN for a given problem domain by automatically searching for the optimal architecture. This paper proposes a new algorithm named, GPCNN, which encodes graph-based CNN architectures as trees and uses genetic operators, i.e. mutation, crossover and selection, to find better architectures. A more flexible crossover, partial subtree crossover, is also proposed to improve the search performance. As an preliminary work, GPCNN did not manage to achieve better performance than the state-of-the-art methods due to the limit on computational resource, but it is able to achieve better results than the baseline methods. More importantly, the proposed tree-based graph representation of CNN allows CNN architecture of various shapes, which has a great potential for future work in evolutionary automatic neural architecture search.