Reviewerly/exHarmony huggingface dataset:
Problems of Methodological Training of Undergraduates in Geographical Education

The problem of improving the methodological training of undergraduates (training area – 44.04.01 Pedagogical education, focus – “Geographical education”), is relevant at the present stage of multi-level pedagogical education development. The relevance is determined by the existing contradiction between the fact that master’s programs are a further continuation of the education of bachelors – future teachers of geography, on the one hand, and on the other hand, any person with a higher education can enroll in a master’s program in this area. Therefore, in one group of undergraduates, specialists and students who are fully prepared in terms of subject and methodological training, who do not have a basic geographical, and, most importantly, pedagogical education, study. Based on this, for some undergraduates, the process of actual improvement of methodological readiness for professional activity is carried out, and for the rest, this mechanism of professional development is just beginning. This article discusses attempts to resolve the indicated contradiction, to determine ways to solve the problem by designing a system of methodological training, consisting of a number of disciplines, each of which forms a number of competencies, both universal and general professional and professional, which are particularly important. This system is distinguished by a variety of organizational forms and teaching methods; special attention is paid to modern technological approaches, such as modular, project-based learning, rating assessment of control and evaluation of learning outcomes. All this, taken together, makes it possible not only to carry out the process of leveling the level of training of all undergraduates, but also to raise it to a higher level. The research methods comprised a theoretical and empirical complex – a system – structural analysis, a comparison of educational and regulatory documentation and scientific and pedagogical literature on the problem of multi-level methodological training of students in the direction of Pedagogical education, long-term supervision of students in classroom and extracurricular activities.
Calculation model for water mass entrained by the water exit of a particle using two projected images captured from orthogonal directions

Water exit is an extremely important phenomenon because it contributes to the development of physics and biology and its application in engineering. In particular, it is necessary to consider the spatiotemporal change in the water mass entrained around an object to correctly understand the scenario of water mass. In this study, a solid particle is launched vertically upward in water toward the surface. The volume of the water mass is estimated using images captured from two orthogonal directions while considering the spatiotemporal development of the water mass. In this study, two water mass estimation methods (Models 1 and 2) are constructed and applied to experimental results of the water exit reported in a previous study by the authors Takamure and Uchiyama (2020a). Model 1 considers the spatiotemporal development of the water mass and estimates the vertical motion of the particle under the influence of water mass with high accuracy when the water mass barely develops around the particle (i.e., submergence depth is small). However, when a large water mass entrains around the particle with an increase in the submergence depth, the estimation accuracy of the volume of the water mass decreases owing to the multilayer structure of the water mass. Model 2 improves the estimation accuracy of the volume of the water mass when the particle covers a large amount of water mass by considering the existence of the multilayer structure of the water mass. For the largest submergence depth in the experiment in this study on the water exit of the particle, the estimation accuracy of the volume of the water mass for Model 1 was approximately 50%, while that for Model 2 was over 80%. Further, the motion of the particle can be estimated with high accuracy even if the water mass is relatively complex and has a multilayer structure by incorporating the effects of the multilayer structure of the water mass into Model 1. This method enables us to estimate the water mass quantity easily and accurately by photographing an object from two orthogonal directions using two synchronized high-speed cameras. This water mass estimation technique is useful for understanding physical phenomena and for engineering applications. • Water mass estimation methods are applied to the water exit of the spherical particle. • The water mass volume is captured from two orthogonal directions. • Estimation accuracy decreases without consideration of multilayer structure. • Estimation accuracy is over 80% with consideration of multilayer structure.
Generating Attractive Advertisement Text Campaigns Using Deep Neural Networks

Text generation task has drawn an increasing attention in the recent years. Recurrent Neural Networks (RNN) achieved great results in this task. There are several parameters and factors that may affect the performance of the recurrent neural networks, that is why text generation is a challenging task, and requires a lot of tuning. This study investigates the impact of three factors that affect the quality of generated text: 1) data source and domain, 2) RNN architecture, 3) named Entities normalization. We conduct several experiments using different RNN architectures (LSTM and GRU), and different datasets (Hulu and booking). Evaluating generated texts is a challenging task. There is no perfect metric judge the quality and the correctness of the generated texts. We use different evaluation metrics to evaluate the performance of the generation models. These metrics include the training loss, the perplexity, the readability, and the relevance of the generated texts. Most of the related works do not consider all these evaluation metrics to evaluate text generation. The results suggest that GRU outperforms LSTM network, and models trained on booking set is better than the ones that trained on Hulu dataset.
Design and Implementation of Strong Security Architecture for Amazon Web Service based on Cloud Applications

Cloud applications are becoming a necessary part of modern life. Security is one of the most important non-functional requirements of every solution. Early days, security and data privacy was just luxury part of software development and it was an optional requirement but nowadays it plays a critical role in daily life. The presented work will be made to observe the need for symmetric security algorithms in Cloud application with Amazon web service. This work observes that the current security level of existing applications recommend improved security solutions to enhance the security level as well performance of proposed architecture. This work recommends Blowfish, RC6 algorithm (symmetric key cryptography) can be used to achieve confidentiality during communication Amazon web service Platform. It also considers the MD5 algorithm to maintain the integrity and modified Kerberos algorithm to achieve authentication. The complete work will propose a security architecture having solution to achieve confidentiality, integrity with strong authentication policy for Cloud application development in Amazon web service. The strong security architecture provide for data and minimum executive time in upload and download file, different key size, file size and chunking size in file. File size divided into chunk 512 bit, 1024 bit, 2048 bit after process in Amazon web service, we are found the optimum time are chunking file size 2048 bits reduce time in encryption and decryption data process and maintain strong security data file in communication including file size 5, 10, 15 and 20 Megabyte.
Basic Knowledge of Database

AbstractVarious database products have different characteristics, but they share some common ground in the main database concepts, that is, they can achieve various database objects and different levels of security protection measures, and they also emphasize the performance management and daily operation and maintenance management of the database.
Getting Started with SQL Syntax

AbstractHuawei GaussDB (for MySQL) is a cloud-based high-performance, high-available relational database that fully supports the syntax and functionality of the open source database MySQL. This chapter introduces GaussDB (for MySQL) data types, system functions and operators to help readers get started with SQL syntax.
Machine Learning

AbstractMachine learning is currently a mainstream research hotspot in the AI industry, entailing multiple disciplines such as probability theory, statistics, and convex optimization. This chapter first introduces the definition of “learning” in learning algorithms and the process of machine learning. On this basis, it offers some commonly used machine learning algorithms. Our readers will learn about some key concepts such as hyperparameters, gradient descent, and cross-validation.
Introduction to Databases

AbstractDatabase technology is a technology developed earlier in computer science, having experienced nearly 60 years of history since its birth in the early 1960s. Now, database technology has developed from the early stage of simply saving and processing data files to a rich comprehensive discipline with data modeling and DBMS as the core, as the foundation and core of modern computer application system. With the continuous refinement of “Internet+”, big data, AI and data mining technologies in recent years, database technology and products are changing day by day. This chapter will give a brief introduction to the basic knowledge and concepts of database.
Database Development Environment

AbstractHuawei's GaussDB (for MySQL) supports the development of applications based on C, Java and other languages. Understanding GaussDB (for MySQL) related system structure and concepts help to develop and use GaussDB (for MySQL) database better.
pygrank: A Python Package for Graph Node Ranking

We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithm components, such as graph filters, post-processors, measures, benchmarks and online tuning. Computations can be delegated to numpy, tensorflow or pytorch backends and fit in back-propagation pipelines. Classes can be combined to define interoperable complex algorithms. Within the context of this paper we compare the package with related alternatives and demonstrate its flexibility and ease of use with code examples.
Enhancing Learning of System Modelling through 360 Degree Virtual Reality Video and 3D Printing

New technologies like 360 degree virtual reality videos and 3D printing are creating philosophical shifts in approaches to teaching and learning in the classroom. However, most studies isolate new technology rather than study the benefits of using them in combination. Multiple representations support a variety of learning activities especially when students are learning new concepts or complex ideas. This study explores the blending of a 360 degree video-case study with 3D-printed haptic manipulations to enhance learning of ICT systems analysis and design. We hypothesize that the blending of both the virtual and physical can lead to improved learning motivation, engagement and enhance learning outcomes. The study follows a design-based research (DBR) methodology with this paper representing the second loop of the DBR approach focusing on both the usability and learning outcomes from a sample of 24 participants from an Australian University. The results of the study show positive impact on the measure of navigability and communication when compared to a traditional lesson leading to improved engagement and motivation among the learners.
Online Versus Face-to-Face: A Quantitative Study of Factors Influencing Students’ Choice of Study Mode using Chi-Square Test and Binary Logistic Regression

&#x0D; &#x0D; &#x0D; For online learning in the January 2019 semester, students at the Singapore University of Social Sciences were able to choose whether they want to study in virtual or face-to-face mode in two courses. Virtual refers to full online learning whereby students study, in a six-week term, without the need to meet face-to-face with the instructor while face-to-face refers to blended e-learning whereby students received either six or three face-to-face lessons with e-learning resources. In full online mode, students will meet the instructor virtually via video conferencing on a weekly basis. Data were obtained to find out which variables actually had an effect of students’ choice of learning mode. 370 students were analysed and the variablesincluding gender, marital status, race, nationality, course, qualification, school, programme, intake, age (now), age (joint) and cumulative grade point average (CGPA) were examined. Each variable was compared with the students’ mode of study in order to identify if they are dependent (e.g. gender versus study mode, race versus study mode, etc.) based on a chi-square test. The significant variables were further investigated using a binary logistic regression model. It was found that qualification, intake and CGPA were found to be significant for students’ choice of learning.&#x0D; &#x0D; &#x0D;
Supporting student writing with an intelligent tutoring system for assignment checking

&#x0D; &#x0D; &#x0D; In this paper we present the results of a prototype system designed as a draft assignment checker that students can use prior to the submission of their assignments. The tool was designed to provide descriptive timely feedback to students on their digitally submitted text- based assignments. This process allows students to submit draft versions of their assignments, obtain feedback and improve them before they make a final submission for marking. Students are able to access the results and descriptive feedback generated for the assignments they have uploaded and the software allows customisation of the evaluation measures based on the type of assignment and expectations of the academic staff. Findings from a survey of student feedback on the system are presented. Overall students found the system useful, but the tool needed to be incorporated into the assignment preparation process more closely to be effective.&#x0D; &#x0D; &#x0D;
Roof-Transformer: Divided and Joined Understanding with Knowledge Enhancement

Recent work on enhancing BERT-based language representation models with knowledge graphs (KGs) and knowledge bases (KBs) has yielded promising results on multiple NLP tasks. State-of-the-art approaches typically integrate the original input sentences with KG triples and feed the combined representation into a BERT model. However, as the sequence length of a BERT model is limited, such a framework supports little knowledge other than the original input sentences and is thus forced to discard some knowledge. This problem is especially severe for downstream tasks for which the input is a long paragraph or even a document, such as QA or reading comprehension tasks. We address this problem with Roof-Transformer, a model with two underlying BERTs and a fusion layer on top. One underlying BERT encodes the knowledge resources and the other one encodes the original input sentences, and the fusion layer integrates the two resultant encodings. Experimental results on a QA task and the GLUE benchmark attest the effectiveness of the proposed model.
Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos

Shoe tread impressions are one of the most common types of evidence left at crime scenes. However, the utility of such evidence is limited by the lack of databases of footwear prints that cover the large and growing number of distinct shoe models. Moreover, the database is preferred to contain the 3D shape, or depth, of shoe-tread photos so as to allow for extracting shoeprints to match a query (crime-scene) print. We propose to address this gap by leveraging shoe-tread photos collected by online retailers. The core challenge is to predict depth maps for these photos. As they do not have ground-truth 3D shapes allowing for training depth predictors, we exploit synthetic data that does. We develop a method termed ShoeRinsics that learns to predict depth by leveraging a mix of fully supervised synthetic data and unsupervised retail image data. In particular, we find domain adaptation and intrinsic image decomposition techniques effectively mitigate the synthetic-real domain gap and yield significantly better depth prediction. To validate our method, we introduce 2 validation sets consisting of shoe-tread image and print pairs and define a benchmarking protocol to quantify the quality of predicted depth. On this benchmark, ShoeRinsics outperforms existing methods of depth prediction and synthetic-to-real domain adaptation.
Grounded Video Situation Recognition

Dense video understanding requires answering several questions such as who is doing what to whom, with what, how, why, and where. Recently, Video Situation Recognition (VidSitu) is framed as a task for structured prediction of multiple events, their relationships, and actions and various verb-role pairs attached to descriptive entities. This task poses several challenges in identifying, disambiguating, and co-referencing entities across multiple verb-role pairs, but also faces some challenges of evaluation. In this work, we propose the addition of spatio-temporal grounding as an essential component of the structured prediction task in a weakly supervised setting, and present a novel three stage Transformer model, VideoWhisperer, that is empowered to make joint predictions. In stage one, we learn contextualised embeddings for video features in parallel with key objects that appear in the video clips to enable fine-grained spatio-temporal reasoning. The second stage sees verb-role queries attend and pool information from object embeddings, localising answers to questions posed about the action. The final stage generates these answers as captions to describe each verb-role pair present in the video. Our model operates on a group of events (clips) simultaneously and predicts verbs, verb-role pairs, their nouns, and their grounding on-the-fly. When evaluated on a grounding-augmented version of the VidSitu dataset, we observe a large improvement in entity captioning accuracy, as well as the ability to localize verb-roles without grounding annotations at training time.
Decoding Polar Codes via Noisy Quantum Gates: Quantum Circuits and Insights

The use of quantum computation for wireless network applications is emerging as a promising paradigm to bridge the performance gap between in-practice and optimal wireless algorithms. While today's quantum technology offers limited number of qubits and low fidelity gates, application-based quantum solutions help us to understand and improve the performance of such technology even further. This paper introduces QGateD-Polar, a novel Quantum Gate-based Maximum-Likelihood Decoder design for Polar error correction codes, which are becoming widespread in today's 5G and tomorrow's NextG wireless networks. QGateD-Polar uses quantum gates to dictate the time evolution of Polar code decoding -- from the received wireless soft data to the final decoded solution -- by leveraging quantum phenomena such as superposition, entanglement, and interference, making it amenable to quantum gate-based computers. Our early results show that QGateD-Polar achieves the Maximum Likelihood performance in ideal quantum simulations, demonstrating how performance varies with noise.
Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language

Research in Natural Language Processing (NLP) has increasingly become important due to applications such as text classification, text mining, sentiment analysis, POS tagging, named entity recognition, textual entailment, and many others. This paper introduces several machine and deep learning methods with manual and automatic labelling for news classification in the Bangla language. We implemented several machine (ML) and deep learning (DL) algorithms. The ML algorithms are Logistic Regression (LR), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), used with Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Doc2Vec embedding models. The DL algorithms are Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), used with Word2vec, Glove, and FastText word embedding models. We develop automatic labelling methods using Latent Dirichlet Allocation (LDA) and investigate the performance of single-label and multi-label article classification methods. To investigate performance, we developed from scratch Potrika, the largest and the most extensive dataset for news classification in the Bangla language, comprising 185.51 million words and 12.57 million sentences contained in 664,880 news articles in eight distinct categories, curated from six popular online news portals in Bangladesh for the period 2014-2020. GRU and Fasttext with 91.83% achieve the highest accuracy for manually-labelled data. For the automatic labelling case, KNN and Doc2Vec at 57.72% and 75% achieve the highest accuracy for single-label and multi-label data, respectively. The methods developed in this paper are expected to advance research in Bangla and other languages.
Hierarchical classification at multiple operating points

Many classification problems consider classes that form a hierarchy. Classifiers that are aware of this hierarchy may be able to make confident predictions at a coarse level despite being uncertain at the fine-grained level. While it is generally possible to vary the granularity of predictions using a threshold at inference time, most contemporary work considers only leaf-node prediction, and almost no prior work has compared methods at multiple operating points. We present an efficient algorithm to produce operating characteristic curves for any method that assigns a score to every class in the hierarchy. Applying this technique to evaluate existing methods reveals that top-down classifiers are dominated by a naive flat softmax classifier across the entire operating range. We further propose two novel loss functions and show that a soft variant of the structured hinge loss is able to significantly outperform the flat baseline. Finally, we investigate the poor accuracy of top-down classifiers and demonstrate that they perform relatively well on unseen classes. Code is available online at https://github.com/jvlmdr/hiercls.