Reviewerly/exHarmony huggingface dataset:
ANALISIS KEMAMPUAN MENULIS KARYA ILMIAH DI KALANGAN MAHASISWA

Writing in tertiary institutions requires requirements both in terms of language, content and writing techniques, because tertiary institutions as an institution that will produce intellectuals, carry out writing activities as an integrated part of the entire teaching and learning process. Having knowledge about the material to be written, related to the above statement, this study discusses the ability to write scientific papers for students who have completed the combined course of writing scientific papers. The purpose of this research is to: (1) measure how students 'ability to write paper scientific papers, (2) students' ability to develop content, (3) students 'ability to organize paper scientific papers, (4) students' ability to determine vocabulary and good terms and right, (5) ability to sort and use appropriate language. This research was carried out using the qualitative descriptive method of population in this study. All 2016 a2 students in the Indonesian language study program who had finished joining the alloy course wrote scientific papers, because the population was too much to take samples randomly from the class so that there were 15 samples to study. . The results of the study were obtained from the results of observations of several papers that have been done by students, (1) from the content aspect the ability to write papers students get the highest score = 30, while the lowest score is 18, and the average score = 25.30. (2) from the organizational organizing aspect to the paper made the highest score = 15, and the lowest score = 13, and the average score 12.10. (3) from the vocabulary aspect and the term ability of students to get the highest score = 15, while the lowest score = 12, and the average = 12.40. (4) from the aspect of language use in a paper made by a student the highest score = 15, and the lowest score = 10, and the average = 13.10. (5) from the spelling and technical aspects of writing scientific papers the students made the highest score = 7, while the lowest score = 6, and the average = 6.10.
Improved scatter search algorithm based on meerkat clan algorithm to solve NP-hard problems

A modified Scatter Search (SS) algorithm based on Meerkat Clan Algorithm (MCA) has been presented in this paper. SS is one of the important metaheuristic algorithms, while the MCA is one of the recent swarm intelligence algorithms. The modified SS algorithm, including the main steps of MCA, through it the diversity and exploration of SS-MCA's solutions, have improved. The proposed algorithm has been applied to two important NP-Hard problems (Travelling Salesman Problem (TSP) and Flexible Job Shop Scheduling Problem (FJSSP)) to verify the performance of SS-MCA. The experimental results show that the performance of SS-MCA is better than both SS and MCA, respectively.
Threat Poker: Gamification of Secure Agile

Agile software development is practiced in most software development projects around the world. To explicitly consider and include security requirements as part of agile software development is referred to as ‘secure agile’. To include security will naturally require additional time and effort, with potentially reduced agility as a consequence. To maintain agility, it is important to have efficient methods to include security in the development process. In this study, we describe enhancements to Threat Poker, which is a game designed for the software development team to deal with security threats identified during the agile development project. Games can be valuable educational tools for actively engaging students and practitioners alike. An experiment with students indicates that playing Threat Poker increases security awareness and that it is a fun and simple way to discuss identified security threats and how to remove security vulnerabilities during the software development process.
Developing an Image-Based 3D Model Editing Method

As 3D technologies advance rapidly, 3D printing, 3D animation, and 3D Movie are springing out in different areas. It becomes a remarkable challenge to generate a large number of 3D models effectively and efficiently. This paper proposes a novel editing method based on the feature lines of images (i.e., image contour and principal axis) for generating new 3D models. Our method takes as input an existing 3D model (as the original model) and an image selected by the user (or a sketch hand-drawn by the users), and performs the model editing to generate a new 3D model. In particular, our method first takes as input an original 3D model and an image selected by the user. Second, the selected image is processed to produce the feature lines, i.e., contour and principal axis of the image. Third, the silhouette of the original model from a given view is acquired, and projected on a projection plane to produce a contour and principal axis of the model, which is the feature lines of the original model. Fourth, by comparing the feature lines of the image and the original model, the constraint conditions are established to control the editing of the 3D model. Finally, 3D model editing is conducted through the as-rigid-as-possible mesh deformation to produce a new 3D model with the appearance resembling the selected image. Furthermore, this paper proposes an energy function to guide the detailed model editing, and measure the similarity between the generated 3D model and the corresponding image. We have conducted extensive experiments to evaluate the proposed method. The results show that comparing with the existing editing methods in literature, the proposed model editing method is able to construct various types of 3D models more effectively and more efficiently.
PRIMARY SCHOOL TEACHER’S PRACTICES AND STUDENT’S MENTAL REPRESENTATIONS: THE LEARNING OBJECTS OPTION

This research focuses on the study of the integration of physical science Learning Objects by primary school teachers. In this paper, we present the results of an empirical study to identify the views of teachers on the quality and adaptation of Learning Objects to mental representations of children to the physical world. The research was carried out using a questionnaire that consisted of 6 questions. The results show that the Learning Objects used are relevant for the elaboration of representations of primary children and that the teachers’ training in this subject is not enough.&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0667/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
An Ecological Perspective on Reading Development: a Theoretical Framework to Guide Empirical Research

&lt;p&gt;Recent efforts have addressed the new challenges related to conceptualizing, understanding and improving reading competency. Several literacy researchers have perceived reading as a developmental skill that is not situated exclusively within student’s cognition, or within family processes, or within classroom or school processes. Rather, reading development has been viewed as a result of the dynamic interaction among reader, family, classroom, and school system (Jaeger, 2017). Following on from this, systems theory approach and more specifically the ecological model allows for the examination of reading skill development from a holistic perspective. It provides an inclusive frame for describing and explaining how the educational opportunities are distributed at the micro, meso, exo, and macro systems and how these systems interact to explain students’ reading differences amon. It also delineates how developing readers’ individual characteristics transact with both proximal and distal processes to craft their reading ecologies. Future policy, practice and research are recommended to be based on the ecological model premises to have a comprehensive view of reading development.&lt;/p&gt;
Towards Deep Unsupervised Representation Learning from Accelerometer Time Series for Animal Activity Recognition

The most compelling reason to use unsupervised representation learning as a feature extraction method for effective animal activity recognition is the ability to learn from unlabeled data. Obtaining labeled data is tedious, labor-intensive, and costly, while it is much easier to obtain raw unlabeled data. In this paper, we compare three unsupervised representation learning techniques with three conventional feature extraction methods that are simple and have excellent performance. To investigate the effect of the size of both labeled and unlabeled parts of the dataset on the quality of the representations, we train the representations and classifier using various sample sizes. Furthermore, we evaluate the effect of depth in feature architectures on the performance of the representation learning techniques. All evaluations are performed on two animal datasets that are diverse in terms of species, subjects, sensor-orientations, and sensor-positions. We demonstrate that unsupervised representation learning techniques approach and, in some cases, outperform engineered features in animal activity recognition.
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness

Ensemble-based Adversarial Training is a principled approach to achieve robustness against adversarial attacks. An important technicality of this approach is to control the transferability of adversarial examples between ensemble members. We propose in this work a simple, but effective strategy to collaborate among committee models of an ensemble model. This is achieved via the secure and insecure sets defined for each model member on a given sample, hence help us to quantify and regularize the transferability. Consequently, our proposed framework provides the flexibility to reduce the adversarial transferability as well as promote the diversity of ensemble members, which are two crucial factors for better robustness in our ensemble approach. We conduct extensive and comprehensive experiments to demonstrate that our proposed method outperforms the state-of-the-art ensemble baselines, at the same time can detect a wide range of adversarial examples with a near perfect accuracy.
Empirical investigation of the associations of technological capability, logistics capability and supply chain management strategies with competitive advantage: Evidence from Saudi manufacturers

The objective of this study is to empirically evaluate the associations of information technology, supply chain management, and logistic capability with a competitive advantage of some Saudi Arabia industry during the year of 2020 using Resource-based Theory.Using the information from a self-administered survey with an ultimate sample of 118 respondents, the outcome of Multiple Regression indicates a significant positive correlations of information technology, supply chain management, and logistics capabilities strategies with a competitive advantage.The outcome of this study should be applied by the company's top management to improve their supply chain management within strategic plans, as a result, increase the magnitude of their competitive advantage and financial performance..
Algorithm for decoding visual gestures for an assistive virtual keyboard

Text production is one of the most frequent activities on a computer, a trivial task that can be limiting for individuals affected by severe neuromotor disorders such as Amyotrophic Lateral Sclerosis (ALS) that can lead to Locked-in syndrome (LIS). These individuals need augmentative and alternative communication tools, since they may have only the eye movements as a form of communication and interaction with the outside world. This work investigates methods of interaction based on eye movement tracking and presents a virtual keyboard that utilizes gaze detection as a text input. It describes the development of the shape detection algorithm for the assistive keyboard, typed word voting from a Brazilian Portuguese lexicon and preliminary results on the decoding algorithm.
Life Hacks, How Tos, and Guides in the Qualitative Research Data Collection Tutorial

A review of the textbook by A.V. Vanke, E.V. Polukhina and A.V. Strelnikova “How to Collect Data in Field Qualitative Research”. The authors of the book describe a joint experience of participation in field research projects, choosing the genre of “how to do” with many examples of field situations, strategies, tools and documents. The problems of access to the field, visual materials collecting, issues of data archiving, and many other topical aspects of research work are reviewed here. The book shows the logic of a field project step by step, from constructing a research design to leaving the field and maintaining connections with it.
Aplikasi Pengenalan Jenis Narkoba Berbasis Android Pada Badan Narkotika Nasional Kota Pagar Alam

Based on observations at the National Narcotics Agency (BNN) Pagar Alam it was found that increased drug abuse among adolescents and their lack of understanding of the dangers posed when consuming drugs. To reduce drug abuse, BNN promotes information on the types of drugs, their impact and prevention of abuse. However, the socialization process incurred considerable costs, was less effective and the lack of interest in the participants to read the brochure because it was monotonous. This research resulted in the introduction of an Android-based type of drug application on BNN Pagar Alam. The system development method is the SDLC method with the waterfall model. With this application can provide information about the types of drugs and how to prevent them.
Twitter Sentiment analysis using different Algorithms

Sentiment analysis is an application of natural language processing.It is also known as emotion extraction or opinion mining.It is a very popular field of research in text mining.The basic idea is to find the polarity of the text and classify it into positive, negative or neutral.Polarity of text is determined from scores identified by VADER.It helps us to understand human decision making or to categorize, analyze and extract opinions from review documents on web sites, blogs, social media, and others in order to understand the consumers.To perform sentiment analysis, there are various algorithms such as SVM, Naïve Bayes and DAN2 which are used to predict the polarities and find their accuracies.There are various tasks like subjectivity detection, sentiment classification, aspect term extraction, feature extraction, keyword selection and keyword analysis etc. that are needed to determine the polarity.
PARALLEL K-MEANS CLUSTERING WITH NAÏVE SHARDING FOR UNSUPERVISED IMAGE SEGMENTATION VIA MPI

In digital image processing, image segmentation is an essential step in which an image is partitioned into groups of pixels. k-means clustering algorithm, which is often considered as fast and efficient, is one of the most widely used clustering algorithms to segment an image. However, as the problem size gets larger, the k-means starts to spend a significant amount of time to process. At this point, parallelization techniques should be applied to reduce the required time. Designing an efficient parallel and distributed model is not a trivial job since it should correspond to the parallel computer architecture and take communication and load balancing among processors into account. In this study, we propose a parallel and distributed k-means clustering algorithm with naive sharding centroid initialization for image segmentation. The proposed algorithm adopts the Message Passing Interface (MPI) standard to take advantage of the computational power of distributed computing nodes in a High Performance Computing Cluster. We demonstrate the parallel scalability of the proposed algorithm using up to 128 cores that achieves approximately 104 times faster clustering time.
Free Aesthetics Society (1906–1917). Chronicle materials

The history of the Moscow Free Aesthetics Society (1906–1917), based on insufficiently and unevenly studied sources, remains on the periphery of research interest. The main purpose of the present publication is to fill this gap. The introduction to the chronicle of the Free Aesthetics Society briefly presents the nature of its activities and its members, including I.I. Troyanovsky, V.Ya. Bryusov, B.N. Bugaev (Andrei Bely), N.R. Kochetov, V.V. Perepletchikov, V.I. Girschan, A.P. Botkin et al. The classification of used sources is made: the society's own reports (handwritten and printed), including financial; agendas and minutes of the Committee proceedings and general meetings; a register of guests at meetings in 1910–1912, preserved in the Manuscript department of the RSL; correspondence of participants; “Literary and Artistic Circle Proceedings”, published since 1913, as well as newspaper reports. The chronicle takes into account all dated official events of the Free Aesthetics Society: first of all, literary and musical evenings, but also the organizational meetings of the Society and its Committee, excluding particular meetings of society members. For the identified meetings, the date, status (general, closed, etc.), venue, topic, start time, conditions of guests admission, other information, including the number of participants and financial results, are given. These are followed with an outline of the main sources: the agenda (if known), a report, etc. After that, documents related to the preparation of the event, its implementation and consequences are listed. Thus, the chronicle describes the main aspects of the Free Aesthetics Society activities, and various sources, many of which have not been published before, are introduced into scientific circulation.
Construction of the University’s Intelligent Teaching Model Based on Mobile Technology—Take the Reform of English Curriculum at Chengdu Neusoft College as an Example

With the development of information technology, coupled with the “University English Teaching Guide” on the integration of information technology and curriculum teaching, China’s college English curriculum began to enter a new era of information-based teaching reform. Based on the teaching reform of English courses in our university, the author constructs the university English intelligent teaching model by implementing the hybrid teaching method based on PBL, realizes precise teaching decision-making, instant feedback and evaluation, three-dimensional interaction and intelligent resource push, and comprehensively transforms the traditional classroom teaching structure and constructs the information classroom teaching model in the era of big data.
Ophiophobia 3D – a Game for Treating Fear of Snakes

Ophiophobia is defined as a pathological state of fear and unease caused by snakes. Taking into consideration the technological advancements made in the last few years, as well as the studies that have proven the efficiency of using virtual reality exposure therapy on patients who suffer from different types of phobias, we set out to develop a 3D environment that would come to the aid of those who suffer from ophiophobia. Therefore, we have created Ophiophobia3D, a game with 2 scenarios, where the player has to collect coins while avoiding or killing snakes that crawl and try to bite. The testing phase of the game was performed with the help of 9 participants who had low and medium levels of ophiophobia. At the end of a testing experiment, we noticed a drop in the fear level of those who explored the 3D environment.
A Hybrid Spherical Search and Sine Cosine Algorithm

Recently, a metaheuristic algorithm called spherical search (SS) has been proposed. The main operations of SS are the calculation of spherical boundary and generation of a new trial solution on the surface of the spherical boundary. Although SS has shown superior performance over many metaheuristic algorithms. It is still easily to fall into local optimum and has poor convergence accuracy. In this paper, we for the first time propose a hybrid spherical search and sine cosine algorithm aimed to leverage the strengths of two different search mechanisms with a co-evolutionary manner. We use the opposite solution to avoid the blind learning from the current optimal solution, to effectively alleviate the immature convergence of the algorithm. Experiments based on 30 benchmark functions of IEEE CEC2017 are conduced and results demonstrate that the proposed algorithm outperforms other states-of-the-art algorithms.
Directional Splines for Economic Analytics

Paper presents the method of constructing a cubic spline for a set of points on a plane.It has been made comparison of the spline with Schoenberg B-spline and Akima and Kathmul-Rom splines.It is shown that for unequally spaced points, at which the disadvantages of the named splines are usually manifested, in comparison with the B-spline, it gives significantly lower oscillations.The spline with such a set of points is practically deprived of the strong kinks that are characteristic of Akima splines.It does not give loops and oscillations, which are a characteristic disadvantage of parametric splines, in particular, Hermitian ones, which includes the Kathmull-Rom spline.The optimization method of spline guide coefficient is proposed, the purpose of which is to minimize discontinuities of the second derivative function at its intermediate points.A fourth-order spline is also proposed, which is deprived of kinks and has lower emissions compared to the Schoenberg spline.The proposed method for blunting sharp peak curves can be applied to all types of splines.
Insights on Training Neural Networks for QUBO Tasks

Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAOA) or quantum annealing (QA). Thus, we consider training neural networks in this context. We first discuss QUBO problems that originate from translated instances of the traveling salesman problem (TSP): Analyzing this representation via autoencoders shows that there is way more information included than necessary to solve the original TSP. Then we show that neural networks can be used to solve TSP instances from both QUBO input and autoencoders' hidden state representation. We finally generalize the approach and successfully train neural networks to solve arbitrary QUBO problems, sketching means to use neuromorphic hardware as a simulator or an additional co-processor for quantum computing.
Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients &#x2013; 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.