Reviewerly/exHarmony huggingface dataset:
VPN Traffic Classification Based on CNN

Classifying network traffic has long been an active field of research, and it is also a key part of network traffic analysis. However, as the use of network traffic encryption and masking grows in the recent years, traditional network classification methods have become less effective. To overcome the traditional methods&#x0027; disadvantages of not being accurate enough to classify real world scenario network traffic that is encrypted and randomly mixed with flows from different services and applications, we proposed a method that utilizes payload-based technique and a convolutional neural network model that was modified from existing network structures. Our data preprocessing converts the byte flow of the packet payload into two dimensional arrays for training and classification. Comparing to existing methods that are not purposed for encrypted traffic classification, our method achieves better accuracy, yet remains less resource-intensive relative to using other deep neural networks, making it more suitable to deploy in network infrastructures for practical use.
Research on Metadata management technology of Navigation and Location Service based on Atlas

For navigation and location services with a large amount of data and data isomerism, space-time characteristics, characteristics of dynamic change, first put forward by unified service metadata standard navigation and position, navigation and location service data information heterogeneity, secondly build navigation and location services under the standard database, based on the Atlas metadata management tools, So as to realize the effective overall management of navigation and location service metadata, efficient query and retrieval, and improve the efficiency of data sharing and application.
Mastering Models in a Quasi-learning Situation of Problem-solving

The purpose of our work was to study the factors that determine the effectiveness of model acquisition in solving inquiry-based problems (balance scale problem). An experimental lesson, which we conducted in the 4th grade, revealed the differences in the way children refer to a general method of assessing equilibrium provided by the teacher. At the end of the lesson a test was conducted. Its results allowed us to divide the participants (22 students) into two groups according to their success, which depended on whether they applied the model, that they had tried out during the lesson. The performance of students in the «Transpositions» test (A.Z. Zak), which was designed to identify students' approach to solving inquiry-based problems («empirical» or «theoretical»), showed significant differences in the level of reflection, analysis, and planning between the two groups (according to the Mann-Whitney criterion p&lt;0.01). These results and data analysis allow us to connect the success of the modeling means' acquisition to the predominance of either an «empirical» or a «theoretical» approach to mastering ways of solving a new problem.
Threshold and infrared singularities: Time evolution, asymptotic state, and entanglement entropy

Threshold and infrared divergences are studied as possible mechanisms of particle production and are compared to the usual decay process in a model quantum field theory from which generalizations are obtained. A spectral representation of the propagator of the decaying particle suggests that decay, threshold, and infrared singularities while seemingly different phenomena are qualitatively related. We implement a nonperturbative dynamical resummation method to study the time evolution of an initial state. It is manifestly unitary and yields the asymptotic state and the distribution function of produced particles. Whereas the survival probability in a decay process falls off as ${e}^{\ensuremath{-}\mathrm{\ensuremath{\Gamma}}t}$, for threshold and infrared divergent cases it falls off instead as ${e}^{\ensuremath{-}\sqrt{t/{t}^{*}}}$ and ${t}^{\ensuremath{-}\mathrm{\ensuremath{\Delta}}}$, respectively, with $\mathrm{\ensuremath{\Gamma}},\mathrm{\ensuremath{\Delta}}\ensuremath{\propto}(\text{coupling}{)}^{2}$, whereas $1/{t}^{*}\ensuremath{\propto}(\text{coupling}{)}^{4}$. Despite the different decay dynamics, the asymptotic state is qualitatively similar: a kinematically entangled state of the daughter particles with a distribution function which fulfills the unitarity condition and is strongly peaked at energy conserving transitions but broadened by the ``lifetime'' $1/\mathrm{\ensuremath{\Gamma}};{t}^{*}$ for usual decay and threshold singularity, whereas it scales with the anomalous dimension $\mathrm{\ensuremath{\Delta}}$ for the infrared singular case. Threshold and infrared instabilities are production mechanisms just as efficient as particle decay. If one of the particles is in a dark sector and not observed, the loss of information yields an entanglement entropy determined by the distribution functions and increases upon unitary time evolution.
The CEFR-Aligned Curriculum: Perspectives of Malaysian Teachers

This paper examines the perceptions of primary school teachers regarding the introduction of the Common European Framework of References for Languages (CEFR) in the Malaysian school curriculum in 2018. This study employed a 4-point Likert scale questionnaire adapted from Nguyen (2017) and Teoh (2018). The data were collected from 500 primary school teachers using a questionnaire that included 11 close ended items and one open ended item. An online version of the questionnaire was distributed to primary school teachers all over Malaysia using the snowball sampling method. The findings showed that the majority of teachers had positively perceived the implementation of CEFR-aligned English curriculum (M=2.82). However, many teachers also indicated that they faced many challenges related to the implementation of the CEFR-aligned curriculum (M=2.24). This study concludes that although the teachers showed a positively viewed the CEFR-aligned curriculum, they may not be able to implement the curriculum successfully as they encountered certain challenges related to it. Curriculum reforms and changes need to take into account teachers’ concerns and provide them with support in order to optimize learning and teaching. Teachers’ concerns and perspectives need to be taken into account as they are major stakeholders that ensure the outcome of language policies.
ASSESSMENT TEACHING AND LEARNING IN AN INTERDISCIPLINARY HIGH SCHOOL PROJECT

The presented article shares a complex system of methods for feedback and assessment of the progress and achievements of students in the implementation of STEM training at the English Language High School "Geo Milev" in Ruse, Bulgaria.In an attempt to draw conclusions about the effectiveness of education through problem based learning (PBL) and to actively involve students in all stages of the learning process, the feedback from students was planned, implemented and analyzed regarding: the strengths and weaknesses of group work on school projects, proposals for improving the effectiveness of the PBL, teamwork, acquired key skills, the degree of satisfaction and self-esteem, the schedule of activities and the set deadlines.The goals set in the implementation of STEM teaching methods are subordinated to the fact that students successfully acquire key skills from the European Framework of Reference / ERR /, to develop their creative potential, to work in a team based on their inherent interests in a developing and supportive environment.The presented paper presents conclusions about the advantages of the interdisciplinary approach for improving the skills for solving problems with the means of computer science. Special attention is paid to the interaction between the teachers, the study of the current progress of the students, the evaluation of the products of the student projects and the evaluation of the team work / of the students and the teachers /. Conclusions are shared about the students' achievements, the satisfaction of the four teachers involved, the challenges and solutions in working together in different subject areas.An integral and important part of learning and teaching is the interpretation of the results by school leaders, teachers and students, so that appropriate adjustments can be made in the methods of work if necessary. The proposed tools are applicable to assess which aspects of existing practice remain appropriate and which need to be rethought and transformed.
Digital twins in pedagogical modeling

The article deals with the possibilities of using modern digital technologies in pedagogical activity on the example of digital twins. The change in the nomenclature of the scientific pedagogical specialty affects the approaches to the implementation of research. At the same time, education should be guided by trends and opportunities offered to society by scientific and technological progress. The digital technology proposed for study is actively used in industry and in the analysis of the behavior of social systems. A digital twin is a digital copy of a living or inanimate physical entity. In the hypothesis under consideration, the pedagogical model will be imitative, and its digital copy will be ontological. Based on the ontological model, we formulate the elements of the system, set mathematical parameters for it and translate it into machine language, prolong the model in time and space and get the result. The form of designing a digital twin of a pedagogical model is presented and described. Research in the field of mathematical modeling and synergetics served as the basis for building a hypothesis. A new approach to pedagogical modeling can serve as a basis for improving vocational education and designing the future professional activities of students.
THE PATTERN OF LEARNER-CONTENT INTERACTION USING INTERACTIVE DIGITAL MATERIALS IN AN ONLINE LEARNING-BASED

In online learning, the interaction between learners and learning materials is very important. The study was started by developing digital content to facilitate learner-content interaction based on the existing printed materials termed Interactive Digital Materials (IDM). This study focuses on investigating the learner-content interaction within an online learning environment, to see how students use the IDM, and whether there is any pattern in how students interact with the content within the IDM. The digital learning material that was used in the study was defined as self-contained and interactive learning material that consists of text-based materials enriched with illustrations and videos. The learning materials used in the study were one course from a graduate program at Universitas Terbuka titled Design and Model of Innovative and Interactive Instructions (MPDR 5203). The study was conducted in two phases. In the first phase of the study, the IDM was provided through a web-based Learning Management System (LMS) and students were expected to read the IDM online. In the second phase, the IDM was provided also through an application-based LMS that was pre-installed in a computer tablet lent to the student, and students were expected to read the IDM either online or offline. Data of students’ interaction with the content in the IDM was collected electronically through the LMS data analytics. The approach taken to gather data on this learner-content interaction was through students’ behavior in reading the content formatted as interactive digital materials.
Exploring the Potential and Suggesting a New Conceptual Framework of Blockchain Technology in Estate Distribution

This paper investigates the potential and advantages of blockchain technology in estate distribution. This is needed for speedy and efficient distribution of estate to the lawful and rightful beneficiaries. Hence, the issues of estate distribution in Malaysia are explored particularly involving documentation management problems whilst application and distribution process. This research provides an extensive review on the issues of estate distribution in Malaysia, the concept and definition of blockchain technology, the potential, the conceptual framework as well as the scenario-based architecture of blockchain technology in estate distribution. The finding reveals that studies on blockchain technology in the distribution of estate have more opportunities and need more studies to be carried out.
TEACHING ROBOTICS AND ONLINE PROGRAMMING USING A VIRTUAL ROBOT

H. Hyksova 1, V. Stoffová 2, V. Gabaľová2 1Palacky University Olomouc (CZECH REPUBLIC) 2University of Trnava (SLOVAKIA)
Video Traffic Volume Extraction Based on Onelevel Feature

In this paper, a single-level feature detector network based on YOLOF is built to detect objects and extract traffic volume information for videos. YOLOF does not have a multi-scale feature fusion structure (such as FPN, PAN). It uses the dilated encoder and uniform matching method to replace the fusion feature module, which simplifies the network structure and improves the response time while maintaining good accuracy. Finally, the target tracking algorithm is used to obtain the traffic flow statistics in the video sequence. Generally speaking, YOLOF has higher detection performance and faster speed than RetinaNet, DETR and other networks of similar structure.
Issues and Risks of Cryptocurrencies – A Case of Bitcoin

Bitcoin is electronic money that uses a public protocol to implement it in a completely decentralized fashion, eliminating the need for it to be managed by a central issuing institution. It has been demonstrated to be a modern payment system that has been utilized in some procedures frequently connected with money laundering or the trafficking of illegal substances of various kinds, although it is still under development. As a result, in this essay, we examine the characteristics that turn a cryptocurrency into a valuable tool for conducting any type of transaction outside of the supervision of any regulatory agency, as well as some of the domains in which its use can lead to new illegal activities.
Spatio-Temporal Graph Convolutional Networks for Earthquake Source Characterization

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Solid Earth. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Spatio-Temporal Graph Convolutional Networks for Earthquake Source CharacterizationAuthorsXitongZhangWillReichard-FlynnMiaoZhangiDMatthewHirniDYouzuoLiniDSee all authors Xitong ZhangLos Alamos National Laboratory (DOE)view email addressThe email was not providedcopy email addressWill Reichard-FlynnLos Alamos National Laboratory (DOE)view email addressThe email was not providedcopy email addressMiao ZhangiDDalhousie UniversityiDhttps://orcid.org/0000-0002-2555-7134view email addressThe email was not providedcopy email addressMatthew HirniDMichigan State UniversityiDhttps://orcid.org/0000-0003-0290-4292view email addressThe email was not providedcopy email addressYouzuo LiniDCorresponding Author• Submitting AuthorLos Alamos National Laboratory (DOE)iDhttps://orcid.org/0000-0001-7337-6760view email addressThe email was not providedcopy email address
Learning Neural Networks without Lazy Weights

Various approaches have been suggested for the regularization of neural networks, including the well-known Dropout and Dropconnect, which are simple and efficient to implement and therefore have been widely used. However, there is a risk of loss of well-trained weights when dropping nodes or weights randomly. In this paper, we propose a regularization method that preserves well-trained weights and removes poorly trained weights. This was motivated by the observation that the trained weights become further trained. We define these as eager weights whereas the opposite as lazy weights. On every weight update, the distribution of the changes in weight values is examined, and the lazy weights are removed layer-wise. The results demonstrate that the proposed method has a faster convergence rate, avoids overfitting, and outperforms competing methods on the classification of benchmark datasets.
IoT and Remote Sensing

Remote sensing is a field of science that involves gathering data and information about any object present on the earth without having a direct contact with the object with the help of sensors. With the advancement in the field of Internet of Things (IoT), this technology has been introduced and technologically blended with remote sensing system to acquire, store, and use the information collected for making complex – problems decision and analysis. This chapter deals with the collateral functioning of IoT and remote sensing systems, and how the former is being considered as an integral part in the latter, presently and also in future. It also deals with the process of data acquisition and data interpretation, followed by the various application areas in which these two technologies are working simultaneously. Also, a little emphasis is placed on the technological relationship between IoT and cutting-edge systems of remote sensing, i.e., Geographical Information System (GIS) and Global Positioning System (GPS).
Decomposition of variation of mixed variables by a latent mixed Gaussian copula model

Many biomedical studies collect data of mixed types of variables from multiple groups of subjects. Some of these studies aim to find the group-specific and the common variation among all these variables. Even though similar problems have been studied by some previous works, their methods mainly rely on the Pearson correlation, which cannot handle mixed data. To address this issue, we propose a latent mixed Gaussian copula (LMGC) model that can quantify the correlations among binary, ordinal, continuous, and truncated variables in a unified framework. We also provide a tool to decompose the variation into the group-specific and the common variation over multiple groups via solving a regularized M-estimation problem. We conduct extensive simulation studies to show the advantage of our proposed method over the Pearson correlation-based methods. We also demonstrate that by jointly solving the M-estimation problem over multiple groups, our method is better than decomposing the variation group by group. We also apply our method to a Chlamydia trachomatis genital tract infection study to demonstrate how it can be used to discover informative biomarkers that differentiate patients.
STUDENTS’ CREATIVITY IN E-LEARNING: CORRELATION WITH DIGITAL COMPETENCE

A. Oskina 1, L. Darinskaia 2 1St. Petersburg State University of Film and Television (RUSSIAN FEDERATION) 2St. Petersburg State University (RUSSIAN FEDERATION)
Findability, Keywords, and the Content in the <i>Journal of Chemical Education</i>

The role of keywords in improving findability of articles published in the Journal of Chemical Education has been developing and progressing for decades. Presently, as computational power and convenience have increased, the ability to consider new paradigms for keyword usage has emerged. Enhanced computing capacity and improved electronic information retrieval create an environment in which authors can choose their own descriptive keywords, rather than rely on the long used but somewhat dated taxonomy of keyword terms supplied by the Journal. The new keyword guidelines and strategies presented to best use them for effective classification of articles represent an important development for authors and readers of the Journal.
Chinese language learners evaluating machine translation accuracy

Li-Ching Chang - With increasingly rapid advances in machine translation (MT) technology, such as Google Translate, MT has become an indispensable learning resource for second or additional language learners.
OrthoAligner: Image-Based Teeth Alignment Prediction via Latent Style Manipulation

In this article, we present OrthoAligner, a novel method to predict the visual outcome of orthodontic treatment in a portrait image. Unlike the state-of-the-art method, which relies on a 3D teeth model obtained from dental scanning, our method generates realistic alignment effects in images without requiring additional 3D information as input and thus making our system readily available to average users. The key of our approach is to employ the 3D geometric information encoded in an unsupervised generative model, i.e., StyleGAN in this article. Instead of directly conducting translation in the image space, we embed the teeth region extracted from a given portrait to the latent space of the StyleGAN generator and propose a novel latent editing method to discover a geometrically meaningful editing path that yields the alignment process in the image space. To blend the edited mouth region with the original portrait image, we further introduce a BlendingNet to remove boundary artifacts and correct color inconsistency. We also extend our method to short video clips by propagating the alignment effects across neighboring frames. We evaluate our method in various orthodontic cases, compare it to the state-of-the-art and competitive baselines, and validate the effectiveness of each component.
Stochastic Data-to-Text Generation Using Syntactic Dependency Information

Data-to-Text Generation (D2T) is one of the most important sub-fields of Natural Language Generation where structured data is transcribed into natural language text. Several solutions have been proposed for D2T so far with relative success, including template-based, phrase structure grammar-based, and neural attention models. However, these methods also have problems such as grammatical flaws, limited naturalness, and semantic deficiencies. In this work, we propose a stochastic corpus-based model for the data-to-text generation that produces a tree-form structure for sentences based on dependency information. This information includes the dependency relations between words and meaning labels extracted from the aligned training sentences parsed with a dependency parser. By combining the dependency relations and meaning labels to construct a tree structure in an up-down manner, each word is placed into the output sentence based on its preceding and succeeding words. This results in fluent sentences with correct grammatical structures. This approach also ensures that all required semantic information are present in the output sentences while irrelevant or redundant labels are avoided. In addition, by using beam search in producing the structure of sentences, the proposed model can generate highly diverse sentences. We test our model on eight domains in tabular, dialogue act, and RDF formats. Our model improves the BLEU by 30% compared to the corpus-based state-of-the-art methods trained on the tabular datasets and also achieves comparable results with the neural network-based approaches trained on dialogue act, E2E, and WebNLG datasets in the BLEU evaluation metric. Furthermore, the value of ERR metric for our results is always zero; that means our model generates sentences without losing any information. Human evaluations show that our model produces high-quality utterances in aspects of informativeness and naturalness as well as quality.