Reviewerly/exHarmony huggingface dataset:
INOVASI LEARNING MANAGEMENT SYSTEM (LMS) MENGGUNAKAN GOOGLE SUITE FOR EDUCATION (GSE) DALAM PEMBELAJARAN JARAK JAUH BIDANG STUDI MATEMATIKA SISWA KELAS IX DI SMP NEGERI 6 PEKALONGAN

&lt;p class="Els-keywords"&gt;This paper aims to determine the impact and learning outcomes of Mathematics Class IX students of SMP Negeri 6 Pekalongan in the 2020/2021 academic year on distance learning using Google Suite for Education (GSE). This paper is a best practice carried out in January-February in the even semester of the 2020/2021 school year. The object of this best practice is 32 students of class IXF at SMP Negeri 6 Pekalongan who were selected randomly (random sampling). The method used is descriptive qualitative research method. Data collection techniques in this study were interviews, observation and documentation. The best practice results show that the use of Google Suite for Education (GSE) can have a positive impact on the mathematics learning process and improve mathematics learning outcomes for class IXF students of SMP Negeri 6 Pekalongan for the 2020/2021 school year. The positive impact is seen from students' activities being more closely monitored and students being active during the learning process and the results of student activity indicators exceeding the target of 70%. The results of learning mathematics are also good, it can be seen from the results of the daily assessment of 32 students, many students who do not complete only 2 people, classical completeness is 94% and the average value is 92. So the completeness value is very high and the average value is also very high.&lt;/p&gt;
E-PixelHop: An Enhanced PixelHop Method for Object Classification

Based on PixelHop and PixelHop++, which are recently developed using the successive subspace learning (SSL) framework, we propose an enhanced solution for object classification, called E-PixelHop, in this work. E-PixelHop consists of the following steps. First, to decouple the color channels for a color image, we apply principle component analysis and project RGB three color channels onto two principle subspaces which are processed separately for classification. Second, to address the importance of multi-scale features, we conduct pixel-level classification at each hop with various receptive fields. Third, to further improve pixel-level classification accuracy, we develop a supervised label smoothing (SLS) scheme to ensure prediction consistency. Forth, pixel-level decisions from each hop and from each color subspace are fused together for image-level decision. Fifth, to resolve confusing classes for further performance boosting, we formulate E-PixelHop as a two-stage pipeline. In the first stage, multi-class classification is performed to get a soft decision for each class, where the top 2 classes with the highest probabilities are called confusing classes. Then,we conduct a binary classification in the second stage. The main contributions lie in Steps 1, 3 and 5.We use the classification of the CIFAR-10 dataset as an example to demonstrate the effectiveness of the above-mentioned key components of E-PixelHop.
Brain over Brawn - Using a Stereo Camera to Detect, Track and Intercept a Faster UAV by Reconstructing Its Trajectory.

The work presented in this paper demonstrates our approach to intercepting a faster intruder UAV, inspired by the MBZIRC2020 Challenge 1. By leveraging the knowledge of the shape of the intruder's trajectory we are able to calculate the interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from ZED Mini to extract the 3D position of the target, for which we devise a histogram-of-depth based processing to reduce noise. Obtained 3D measurements of target's position are used to calculate the position, the orientation and the size of a figure-eight shaped trajectory, which we approximate using lemniscate of Bernoulli. Once the approximation is deemed sufficiently precise, measured by Hausdorff distance between measurements and the approximation, an interception point is calculated to position the intercepting UAV right on the path of the target. The proposed method, which has been significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. The results confirmed that an efficient visual perception module which extracts information related to the motion of the target UAV as a basis for the interception, has been developed. The system is able to track and intercept the target which is 30% faster than the interceptor in majority of simulation experiments. Tests in the unstructured environment yielded 9 out of 12 successful results.
Leveraging Existing Technology: Developing a Trusted Digital Repository for the U.S. Geological Survey

&#x0D; As Federal Government agencies in the United States pivot to increase access to scientific data (Sheehan, 2016), the U.S. Geological Survey (USGS) has made substantial progress (Kriesberg et al., 2017). USGS authors are required to make federally funded data publicly available in an approved data repository (USGS, 2016b). This type of public data product, known as a USGS data release, serves as a method for publishing reviewed and approved data. In this paper, we present major milestones in the approach the USGS took to transition an existing technology platform to a Trusted Digital Repository. We describe both the technical and the non-technical actions that contributed to a successful outcome.We highlight how initial workflows revealed patterns that were later automated, and the ways in which assessments and user feedback influenced design and implementation. The paper concludes with lessons learned, such as the importance of a community of practice, application programming interface (API)-driven technologies, iterative development, and user-centered design. This paper is intended to offer a potential roadmap for organizations pursuing similar goals.&#x0D; &#x0D;
Convergence analysis of beetle antennae search algorithm and its applications

The beetle antennae search algorithm was recently proposed and investigated for solving global optimization problems. Although the performance of the algorithm and its variants were shown to be better than some existing meta-heuristic algorithms, there is still a lack of convergence analysis. In this paper, we provide theoretical analysis on the convergence of the beetle antennae search algorithm. We test the performance of the BAS algorithm via some representative benchmark functions. Meanwhile, some applications of the BAS algorithm are also presented.
Technology in culture: Embodiment of the divine and transformation of the natural (N.F. Fedorov, P.A. Florensky, F. Dessauer)

The relevance of the research topic is related to the most important religious and philosophical concepts of the late 19th early 20th centuries, in which the mission of technology was defined as a special way of returning to the integrity of being.&#x0D; The purpose of the article is to consider the ideas of N.F. Fedorov, P.A. Florensky, F. Dessauer on the role of technology in the embodiment of the divine purpose and the transformation of the world.&#x0D; Comparative approach was used in the study as the main methodological approach. It contributed not only to the disclosure of the features of the authors ideas, but also made it possible to draw a conclusion about the general trends in determining the ontological status of technology in the religious and philosophical works of the period under consideration.&#x0D; In the conclusion of the article, it is noted that the purpose of technology in the concepts of N.F. Fedorov, P.A. Florensky, F. Dessauer is associated with its ability to overcome decay and restore unity: man with God, divine with natural, natural with social, etc. through culture, understood as human activity, sanctified by the Creators purpose.
Supervised Contrastive Learning for Accented Speech Recognition

Neural network based speech recognition systems suffer from performance degradation due to accented speech, especially unfamiliar accents. In this paper, we study the supervised contrastive learning framework for accented speech recognition. To build different views (similar "positive" data samples) for contrastive learning, three data augmentation techniques including noise injection, spectrogram augmentation and TTS-same-sentence generation are further investigated. From the experiments on the Common Voice dataset, we have shown that contrastive learning helps to build data-augmentation invariant and pronunciation invariant representations, which significantly outperforms traditional joint training methods in both zero-shot and full-shot settings. Experiments show that contrastive learning can improve accuracy by 3.66% (zero-shot) and 3.78% (full-shot) on average, comparing to the joint training method.
Modelling Net Radiative Measurement of Meteorological Parameters Using MERRA-2 Data in Sub-Sahara African Town

In this study, the net radiation was estimated using a simple straightforward expression proposed by different researchers, which is based on the principle of the Fourier Series Technique. The estimation of net radiation of Iwo ( ) from the data collected from the archive of HelioClim satellite MERRA- 2 (i.e. global solar radiation and air temperature) was done on the real and imaginary measurements. The result of both real and imaginary radiation at maximum revealed ( ) and minimum at about ( ), while solar radiation and temperature revealed about ( ) and 299K maximum and minimum ( ) and 297.7K, respectively. Statistically, the result indicated that the regression coefficient of 3.959 with t- statistics of 3.34 and p < 0.05 indicates that for every 1K increase in air temperature, solar radiation will increase by 3.959, which shows that both solar radiation and temperature have a significant effect on net radiation. Therefore, the researchers concluded that Iwo had maximum real net radiation in February with months such as January, March, July, August, October and December as minimum radiation while imaginary radiation had its maximum and minimum in September and August respectively.
Unity Perception: Generate Synthetic Data for Computer Vision

We introduce the Unity Perception package which aims to simplify and accelerate the process of generating synthetic datasets for computer vision tasks by offering an easy-to-use and highly customizable toolset. This open-source package extends the Unity Editor and engine components to generate perfectly annotated examples for several common computer vision tasks. Additionally, it offers an extensible Randomization framework that lets the user quickly construct and configure randomized simulation parameters in order to introduce variation into the generated datasets. We provide an overview of the provided tools and how they work, and demonstrate the value of the generated synthetic datasets by training a 2D object detection model. The model trained with mostly synthetic data outperforms the model trained using only real data.
Hermite Interpolation With Error Correction

Multiplicity code decoders are based on Hermite polynomial interpolation with error correction. In order to have a unique Hermite interpolant one assumes that the field of scalars has characteristic 0 or ≥ 𝓁 +1, where 𝓁 is the maximum order of the derivatives in the list of values of the polynomial and its derivatives which are interpolated. For scalar fields of characteristic 𝓁+1, the minimum number of values for interpolating a polynomial of degree ≤ D is D+1+2E(𝓁+1) when ≤ E of the values are erroneous. Here we give an error-correcting Hermite interpolation algorithm that requires fewer values, that is, that can tolerate more errors, assuming that the characteristic of the scalar field is either 0 or ≥ D+1. Our algorithm requires (𝓁+1)D + 1 - (𝓁+1)𝓁/2 + 2E values.
Building an e-Learning Application Using Multi-agents and Fuzzy Rules

One of the biggest challenges in education is teaching mathematics, especially to children. It has been proven that difficulties students face when learning basic mathematics are often the result of previously acquired misconceptions. These misconceptions prevent the student from understanding new concepts and will eventually create a psychological barrier that prevents the student from learning more advanced mathematics. The conventional classroom environment does not provide the teacher with the most efficient means to detect and correct such misconceptions. The goal of our research is to develop an e-learning system for basic mathematics that is capable of providing each student with personalized content to overcome these misconceptions. The system uses a multi-agent architecture to monitor the activity of the student while simultaneously observing and modeling the student’s knowledge and misconceptions. Lessons and exam questions are chosen dynamically by the multi-agent system to cover the prerequisites of new lessons depending on the profile of the user.
Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning

In recent years, the fast rise in number of studies on graph neural network (GNN) has put it from the theories research to reality application stage. Despite the encouraging performance achieved by GNN, less attention has been paid to the privacy-preserving training and inference over distributed graph data in the related literature. Due to the particularity of graph structure, it is challenging to extend the existing private learning framework to GNN. Motivated by the idea of split learning, we propose a \textbf{S}erver \textbf{A}ided \textbf{P}rivacy-preserving \textbf{GNN} (SAPGNN) for the node level task on horizontally partitioned cross-silo scenario. It offers a natural extension of centralized GNN to isolated graph with max/min pooling aggregation, while guaranteeing that all the private data involved in computation still stays at local data holders. To further enhancing the data privacy, a secure pooling aggregation mechanism is proposed. Theoretical and experimental results show that the proposed model achieves the same accuracy as the one learned over the combined data.
DESTIN: Detecting State Transitions in Network elements

Operators are interested in gaining a comprehensive assessment of their network elements and tracking operational changes. Commonly, this assessment is achieved by performing regular checks of different operational counters and defining expert rules from known root causes. The common approach requires the maintenance of a regularly updated set of rules and only goes as far as the operator’s pre-gained knowledge of the system. In this paper, a broader set of counters (not limited to the handpicked Key Performance Indicators (KPIs)) is explored with an unsupervised approach. The goal is to leverage the dependencies between the counters in order to discover complex state changes that might have otherwise slipped the operator’s view. This paper proposes DESTIN, a multivariate unsupervised change detection for high dimensional time-series data of originally low effective dimension, which provides near real-time state assessment of network device. The efficiency of the method is demonstrated on an experimental test-bed.
DISCRETE APPROXIMATION OF CONTINUOUS OBJECTS WITH MATLAB

This work is dedicated to the study of various discrete approximation methods for continuous links, which is theobligatory step in the digital control systems synthesis for continuous dynamic objects and the guidelines development for performing these opera-tions using the MATLAB programming system. The paper investigates such sampling methods as pulse-, step-, and linearly invariant Z-transformations, substitution methods based on the usage of numerical integration various methods and the zero-pole correspond-ence method. The paper presents examples of using numerical and symbolic instruments of the MATLABto perform these opera-tions, offers an m-function improved version for continuous systems discretization by the zero-pole correspondence method, which allows this method to approach as step-invariant as linearly invariant Z-transformations; programs for continuous objects discrete approximation in symbolic form have been developed, which allows to perform comparative analysis of sampling methods and sys-tems synthesized with their help and to study quantization period influence on sampling accuracy by analytical methods. A compari-son of discrete transfer functions obtained by different methods and the corresponding reactions in time to different signalsis per-formed. Usingof the developed programs it is determined that the pulse-invariant Z-transformationcan be usedonly when the input of a continuous object receives pulse signals, and the linear-invariant transformation should be used for intermittentsignals at the input. The paper also presents an algorithm for applying the Tustin method, which corresponds to the replacement of analogueinte-gration by numerical integrationusingtrapezoidal method. It is shown that the Tustin method is the most suitable for sampling of first-order regulators with output signal limitation. The article also considers the zero-pole correspondence method and shows that it has the highest accuracy among the roughmethods of discrete approximation.Based on the performed research, recommendations for the use of these methods in the synthesis of control systems for continuous dynamic objects are given.
Alfabetismos transmedia y construccionismo como elementos para el rediseño didáctico. Un estudio de caso en el contexto universitario

This paper takes as a starting point a diagnosis shared by different authors regarding the decoupling betweenthe media practices of young people inside and outside of educational institutions (Baricco, 2008; Corea & Lewkowickz, 2004; Jenkins, 2008, Scolari, 2018; Serres, 2013). In a second instance it proposes the constructionism of Seymour Papert and the concept of transmedia literacy as a productive tool for understanding and redesigning the didactic in the current ecology of media. Finally, a specific didactic strategy designed under the premise of minimizing these dissonances from the incorporation and expansion of transmedia media literacy is described and analyzed.
Introduction of mixed forms of education based on the digital and analog kinds integration of the organization of the educational process of universities

The abrupt transition to the distance learning format during the COVID-19 pandemic showed that the legal framework for regulating the introduction of new digital telecommunications technologies is largely lagging behind in educational organizations, and in some areas, in particular, related to the regulation of “remote” labor relations in higher education is completely absent. The lack of proper legal regulation leads not only to labor conflicts, but also to legal proceedings between students and universities. The article presents an incomplete list of documents (without disclosing their contents) that need to be developed or modified. The study proposes a roadmap that takes into account the parallel work with the constant coordination of the contents of the documents between the working groups. The authors conclude that trade union committees, student and teaching communities should take an active part in the development of documentation, since the proposed transformation of the educational process affects all participants of the educational process in universities without exception.
Strategi Dosen Dalam Manajemen E-Learning Guna Meningkatkan Hasil Belajar Mahasiswa di Perguruan Tinggi

The purpose of this research is to find out how lecturers' strategies in e-learning management to improve learning outcomes in college. In this study, strategi lecturer in e-learning management in question includes how to plan, implement, supervise, and evaluate in online learning (e-learning). This type of research uses a qualitative approach with descriptive methods. The subjects of the study are lecturers who do online learning and are able to improve the results of learning through e-learning. The research instrument uses interview guidelines and documentation. The data collection technique in this study is an interview with a strengthened on website e-learning display website documentation. Data analysis using data triangulation techniques. The results of this study showed that the lecturer's strategy in managing e-learning, including 1) Planning, namely drawing up a learning plan on the e-learning website, with a sub-view of RPS (lesson plan), learning achievements, diagnostic tests (pre-test), module materials, learning videos, journal links related to materials, discussion forums, tasks, and absorption tests (posttest). 3) Supervision is by utilizing the agreed e-learning time, monitoring process through zoom media, video conference and whatsapp group to know the activeness and atamodity of students in e-learning. 4) Evaluation, with diagnostic tests and absorption tests as an indicator of student atteanity in receiving e-learning materials and independent tasks to improve HOTS (higher order of thinking skills) students. Evaluation in maximizing e-learning content in the website is done with CIPP (context, input, process, and output) so that innovation from lecturers is always increasing.
Compressing and Indexing Aligned Readsets

Compressed full-text indexes are one of the main success stories of bioinformatics data structures but even they struggle to handle some DNA readsets. This may seem surprising since, at least when dealing with short reads from the same individual, the readset will be highly repetitive and, thus, highly compressible. If we are not careful, however, this advantage can be more than offset by two disadvantages: first, since most base pairs are included in at least tens reads each, the uncompressed readset is likely to be at least an order of magnitude larger than the individual’s uncompressed genome; second, these indexes usually pay some space overhead for each string they store, and the total overhead can be substantial when dealing with millions of reads.The most successful compressed full-text indexes for readsets so far are based on the Extended Burrows-Wheeler Transform (EBWT) and use a sorting heuristic to try to reduce the space overhead per read, but they still treat the reads as separate strings and thus may not take full advantage of the readset’s structure. For example, if we have already assembled an individual’s genome from the readset, then we can usually use it to compress the readset well: e.g., we store the gap-coded list of reads' starting positions; we store the list of their lengths, which is often highly compressible; and we store information about the sequencing errors, which are rare with short reads. There is nowhere, however, where we can plug an assembled genome into the EBWT.In this paper we show how to use one or more assembled or partially assembled genome as the basis for a compressed full-text index of its readset. Specifically, we build a labelled tree by taking the assembled genome as a trunk and grafting onto it the reads that align to it, at the starting positions of their alignments. Next, we compute the eXtended Burrows-Wheeler Transform (XBWT) of the resulting labelled tree and build a compressed full-text index on that. Although this index can occasionally return false positives, it is usually much more compact than the alternatives. Following the established practice for datasets with many repetitions, we compare different full-text indices by looking at the number of runs in the transformed strings. For a human Chr19 readset our preliminary experiments show that eliminating separators characters from the EBWT reduces the number of runs by 19%, from 220 million to 178 million, and using the XBWT reduces it by a further 15%, to 150 million.