Reviewerly/exHarmony huggingface dataset:
Klasifikasi Tutupan Lahan Menggunakan Google Earth Engine

Land cover information is very important information to describe a phenomenon from various aspects that occur on the earth's surface. Land cover information can be obtained using remote sensing technology which can provide spatial information on the earth's surface quickly and precisely, one of the methods used is supervised classification. The rapid development of technology has an impact on satellite image processing methods, one of which is the Google Earth Engine (GEE). This cloud computing-based platform allows users to process satellite images that can be accessed online and free. The conventional method that is commonly used in land cover classification takes a long time because the process of downloading satellite image data must be carried out and continued with data processing using satellite image processing software, otherwise, a computer that has high performance is required so that the image processing process can run smoothly. GEE provides convenience because processing can be done anywhere and anytime without being limited by hardware specifications which often become obstacles in conventional satellite image processing. This study aims to classify land cover using the cloud computation method found in GEE. The level of confidence in the results of the land cover classification was tested using the confusion matrix. The result of the accuracy shows that the overall accuracy level is 96.458% and the kappa accuracy is 93.851%. The classification results show that image processing based on GEE cloud computing can produce a good land cover classification.
Segment- and Arc-Based Vectorizations by Multi-scale/Irregular Tangential Covering

In this paper, we propose an original manner to employ a tangential cover algorithm - minDSS - in order to vectorize noisy digital contours. To do so, we exploit the representation of graphical objects by maximal primitives we have introduced in previous work. By calculating multi-scale and irregular isothetic representations of the contour, we obtained 1-D (one-dimensional) intervals, and achieved afterwards a decomposition into maximal line segments or circular arcs. By adapting minDSS to this sparse and irregular data of 1-D intervals supporting the maximal primitives, we are now able to reconstruct the input noisy objects into cyclic contours made of lines or arcs with a minimal number of primitives. We explain our novel complete pipeline in this work, and present its experimental evaluation by considering both synthetic and real image data.
MPEG column: 125th MPEG meeting in Marrakesh, Morocco

The original blog post can be found at the Bitmovin Techblog and has been modified/updated here to focus on and highlight research aspects. The 125th MPEG meeting concluded on January 18, 2019 in Marrakesh, Morocco with the following topics: Network-Based Media Processing (NBMP) - MPEG promotes NBMP to Committee Draft stage 3DoF+ Visual - MPEG issues Call for Proposals on Immersive 3DoF+ Video Coding Technology MPEG-5 Essential Video Coding (EVC) - MPEG starts work on MPEG-5 Essential Video Coding ISOBMFF - MPEG issues Final Draft International Standard of Conformance and Reference software for formats based on the ISO Base Media File Format (ISOBMFF) MPEG-21 User Description - MPEG finalizes 2nd edition of the MPEG-21 User Description The corresponding press release of the 125th MPEG meeting can be found here. In this blog post I'd like to focus on those topics potentially relevant for over-the-top (OTT), namely NBMP, EVC, and ISOBMFF.
Lexd: A Finite-State Lexicon Compiler for Non-Suffixational Morphologies

This paper presents lexd, a lexicon compiler for languages with non-suffixational morphology, which is intended to be faster and easier to use than existing solutions while also being compatible with other tools. We perform a case-study for Chukchi, comparing against a hand-optimised analyser written in lexc, and find that while lexd is easier to use, performance remains an obstacle to its use at production level. We also compare performance between lexd and hfst-lexc for three analysers still in the prototype phase, finding that lexd is at least as fast, sometimes faster, to compile; we conclude it is a reasonable choice for prototyping new analysers. Future work will explore how to move lexd performance toward production-grade.
Building a Knowledge Graph of Vietnam Tourism from Text

Most data in the world is in form of text. Therefore, we can say text stores large amount of the knowledge of human beings. Extracting useful knowledge from text, however, is not a simple task. In this paper, we present a complete pipeline to extract knowledge from paragraph. This pipeline combines state-of-the-art systems in order to yield optimal results. There are some other Knowledge Graphs such as Google Knowledge Graph, YAGO, or DBpedia. Most of the data in these Knowledge Graphs is in English. On the other hand, the results from our system is used to build a new Knowledge Graph in Vietnamese of Vietnam Tourism. We use the rich resources language like English to process a low resources language like Vietnamese. We utilize the NLP tools of English such as Google translate, Stanford parser, Co-referencing, ClausIE, MinIE. We develop Google Search to find the text describing the entities in the Internet. This text is in Vietnamese. Then, we translate the Vietnamese text into English text and use English NLP tools to extract triples. Finally, we translate the triples back into Vietnamese and build the knowledge graph of Vietnam tourism. We conduct experiment and discover the advantages and disadvantages of our method.
Sheaf Neural Networks.

We present a generalization of graph convolutional networks by generalizing the diffusion operation underlying this class of graph neural networks. These sheaf neural networks are based on the sheaf Laplacian, a generalization of the graph Laplacian that encodes additional relational structure parameterized by the underlying graph. The sheaf Laplacian and associated matrices provide an extended version of the diffusion operation in graph convolutional networks, providing a proper generalization for domains where relations between nodes are non-constant, asymmetric, and varying in dimension. We show that the resulting sheaf neural networks can outperform graph convolutional networks in domains where relations between nodes are asymmetric and signed.
BASICS OF PRODUCTION AS A SYSTEM-FORMING COMPONENT OF PROFESSIONAL TRAINING OF A MODERN TEACHER OF NATURAL SCIENTIFIC AND TECHNOLOGICAL CYCLES

The complication of the technogenic sphere is proceeding at an accelerating pace; therefore, more and more developed technological competencies are required from a subject of activity. The intensification of the technological sphere requires its reflection in the field of professional training of a modern teacher. The purpose of the article is to justify the need to revise the content, as well as the choice of means and methods, of professional training in connection with the formation of a new technological structure in society, based on a continuous change in the nature of production. A set of psychological and pedagogical conditions that ensure the effectiveness of professional training of students is highlighted. The materials of the article can be used by authors and developers of new approaches and concepts in the modernization of the content and technologies of professional training of a modern teacher
The Modern education in the period of pandemic: problems and their solutions

Examined is the collision of the COVID-19 pandemic on the modern education system. The COVID-19 pandemic knows no boundaries. It affected people regardless of nationality, education level, income or gender. The pandemic has affected all aspects of social life, including education. This article analyzed measures taken by various states around the world. The ways of ensuring continuous and universal education, as well as access to it for students (electronic devices, access to the Internet) have been studied. Conclusion is made, that an effective response to emergency situations requires individual, organizational and institutional capacity.
PARTICIPATIVE DIGITAL CULTURE OF THE FUTURE TEACHER: THE POSSIBILITIES OF EDMODO EDUCATIONAL PLATFORM

This article reveals the features of using the Edmodo educational platform as one of the tools for the formation of a participatory digital culture of a future teacher. The authors present the stages of work on this platform, describe the current results of a pedagogical experiment on the implementation of a system for the formation of a participatory digital culture of a future teacher. The article contains an overview of the multidimensional implementation of educational tools, ways of combining and complementing them, elements of the analysis of reviews of types of users' involvement in the process of participation. The authors emphasize the active position of the personality of participants in the educational process in a participatory aspect when changing and sharing educational digital content. The article defines the role of participation as a key link in the tandem of mediated communication between subjects of educational activity in the digital space. The authors present the results of an experiment in which 125 students of the "Foreign language: two foreign languages" major were trained to use the Edmodo platform as part of the "Teacher's professional guidelines" discipline. The training in the use of this digital tool was based on a participatory approach, which implies taking into account the personal qualities of users in the process of joint creation, implementation, analysis and interpretation of the current and final results of digital content.
A Novel Hybrid Algorithm: Sine Cosine Harmony Search Algorithm for Global Optimization

The study is about the new hybrid optimization algorithm called Sine Cosine Harmony Search (SCHS). SCHS was created by combining the features of Sine Cosine Algorithm (SCA) and Harmony Search (HS) meta-heuristic algorithms. In the research, the stage of creating the model was confirmed by experiments. For this, it has been tested using global optimization techniques. Benchmark Functions (BF) with different parametric properties were used for testing time. For performance measurement, comparisons were made with various optimization algorithms. The improvement of SCHS on the basis of exploitation and exploration shows that the algorithm is competitive.
Split Balancing (sBal)—A Data Preprocessing Sampling Technique for Ensemble Methods for Binary Classification in Imbalanced Datasets

The problem of class imbalance in machine learning occurs when there is a relatively big disproportional distribution of classes in the data for classification tasks. In many real-world domains, such as in healthcare, finance, and predictive maintenance, the number of data points of a less important class (usually the negative class) is much higher than the class of greater interest (usually the positive or target class). This affects the ability of many learning algorithms to find good classification models. To address that, many approaches for solving this problem have been proposed, prominently including ensemble methods integrated with sampling-based techniques. However, these methods are still prone to the negative effects of sampling-based techniques that alter class distributions via over-sampling or under-sampling, which can lead to overfitting or discarding useful data, respectively, and thus affect performance. In this paper, we propose a new data preprocessing sampling technique dubbed as (sBal) for ensemble methods for binary classification in the case of imbalanced datasets. Our proposed method first turns the imbalanced dataset into several balanced bins/bags. Then multiple base learners are induced on the balanced bags and finally, the classification results are combined using a specific ensemble rule. We evaluated the performance of our proposed method on 50 imbalanced real-world binary datasets and compared its performance with well-known ensemble methods that utilize data preprocessing techniques namely SMOTEBagging, SMOTEBoost, RUSBoost, and RAMOBoost. The results reveal that the proposed method brings considerable improvement in classification performance relevant to the compared methods. We performed statistical significance analysis using Friedman’s non-parametric statistical test with Bergman post-hoc test. The analysis showed that our method performed significantly better than the majority of the methods across many datasets, suggesting a better preprocessing approach than the ones used in compared methods. We also highlight possible extensions to the method that can improve its effectiveness.
An evaluation of a three-modal hand-based database to forensic-based gender recognition

In recent years, behavioural soft-biometrics have been widely used to improve biometric systems performance. Information like gender, age and ethnicity can be obtained from more than one behavioural modality. In this paper, we propose a multimodal hand-based behavioural database for gender recognition. Thus, our goal in this paper is to evaluate the performance of the multimodal database. For this, the experiment was realised with 76 users and was collected keyboard dynamics, touchscreen dynamics and handwritten signature data. Our approach consists of compare two-modal and one-modal modalities of the biometric data with the multimodal database. Traditional and new classiﬁers were used and the statistical Kruskal-Wallis to analyse the accuracy of the databases. The results showed that the multimodal database outperforms the other databases.
Multipartite quantum systems: an approach based on Markov matrices and the Gini index

Abstract An expansion of row Markov matrices in terms of matrices related to permutations with repetitions, is introduced. It generalises the Birkhoff–von Neumann expansion of doubly stochastic matrices in terms of permutation matrices (without repetitions). An interpretation of the formalism in terms of sequences of integers that open random safes described by the Markov matrices, is presented. Various quantities that describe probabilities and correlations in this context, are discussed. The Gini index is used to quantify the sparsity (certainty) of various probability vectors. The formalism is used in the context of multipartite quantum systems with finite dimensional Hilbert space, which can be viewed as quantum permutations with repetitions or as quantum safes. The scalar product of row Markov matrices, the various Gini indices, etc, are novel probabilistic quantities that describe the statistics of multipartite quantum systems. Local and global Fourier transforms are used to define locally dual and also globally dual statistical quantities. The latter depend on off-diagonal elements that entangle (in general) the various components of the system. Examples which demonstrate these ideas are also presented.
Average-Case Algorithms for Testing Isomorphism of Polynomials, Algebras, and Multilinear Forms.

We study the problems of testing isomorphism of polynomials, algebras, and multilinear forms. Our first main results are average-case algorithms for these problems. For example, we develop an algorithm that takes two cubic forms f, g ∈ 𝔽_q[x_1, … , x_n], and decides whether f and g are isomorphic in time q^O(n) for most f. This average-case setting has direct practical implications, having been studied in multivariate cryptography since the 1990s. Our second result concerns the complexity of testing equivalence of alternating trilinear forms. This problem is of interest in both mathematics and cryptography. We show that this problem is polynomial-time equivalent to testing equivalence of symmetric trilinear forms, by showing that they are both Tensor Isomorphism-complete (Grochow & Qiao, ITCS 2021), therefore is equivalent to testing isomorphism of cubic forms over most fields.
Program Kampung Literasi dalam Menumbuhkan Minat Baca Siswa SD/MI Desa Pandulangan Hulu Sungai Selata

This study aims to describe the implementation of the Kampung Literasi program in increasing the reading interest of the students in Pandulang Village, Padang Batung Subdistrict and to describe the inhibiting and supporting factors of the Kampung Literasi Program in Pandulang Village. This research uses a case study method with a qualitative approach. This research uses observation, interview, and documentation to collect the data. The data analysis technique used in this research is analysis of qualitative data, data reduction, data presentation, and conclusion. Test the validity of the data in this study using triangulation. Triangulation in credibility testing is defined as checking data from various ways and at various times. Based on the results of the study, it can be concluded that the Kampung Literasi program is running well and that students frequently visit every Friday because every Friday morning there is a visit from the Library and Archives Office of Hulu Sungai Selatan Regency in the form of a reading park car and smart motorbikes. The inhibiting factors for the the Kampung Literasi program in Pandulang village were students' lack of motivation towards reading interest, inadequate space, and weather factors. Meanwhile, the supporting factors for the Literacy Village program in Pandulang village were facilities and infrastructure, the availability of reading materials and government support.
Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline

High-resolution remote sensing images are the key data source for the visual system of a flight simulator for training a qualified pilot. However, due to hardware limitations, it is an expensive task to collect spectral and spatial images at very high resolutions. In this work, we try to tackle this issue with another perspective based on image super-resolution (SR) technology. First, we present a new ultra-high-resolution remote sensing image dataset named Airport80, which is captured from the airspace near various airports. Second, a deep learning baseline is proposed by applying the generative and adversarial mechanism, which is able to reconstruct a high-resolution image during a single image super-resolution. Experimental results for our benchmark demonstrate the effectiveness of the proposed network and show it has reached satisfactory performances.
Efficient Field of Vision Algorithms for Large 2D Grids

This paper presents new algorithms for Field of Vision (FOV) computation which improve on existing work at high resolutions. FOV refers to the set of locations that are visible from a specific position in a scene of a computer game. We review existing algorithms for FOV computation, describe their limitations, and present new algorithms which aim to address these limitations. We first present an algorithm which makes use of spatial data structures in a way which is new for FOV calculation. We then present a novel technique which updates a previously calculated FOV, rather than re-calculating an FOV from scratch. We compare our algorithms to existing FOV algorithms and show they provide substantial improvements to running time. Our algorithms provide the largest improvement over existing FOV algorithms at highresolutions, thus allowing the possibility of the creation of high resolution FOV-based video games.
Conjunctive Queries: Unique Characterizations and Exact Learnability

We answer the question of which conjunctive queries are uniquely characterized by polynomially many positive and negative examples, and how to construct such examples efficiently. As a consequence, we obtain a new efficient exact learning algorithm for a class of conjunctive queries. At the core of our contributions lie two new polynomial-time algorithms for constructing frontiers in the homomorphism lattice of finite structures. We also discuss implications for the unique characterizability and learnability of schema mappings and of description logic concepts.
Online Learning For Primary Level / Madrasah With Online Applications In Min 2 Serdang Bedagai

The online learning system is still having problems. As a result of the observations of the USU team, several obstacles were felt not only by the teachers at MIN 2 Serdang Bedagai but also by students and parents. The problems that occur are quite diverse. Starting from limited signal, limited devices suitable for online learning, to the lack of technological knowledge of teachers and students on online applications that support the teaching and learning process. There are various online applications available today, such as Google Classroom, Zoom, Google Meet, Youtube, and others. These various online applications play an important role in helping students and teachers in the distance learning process. The USU team conducted training using the online application Google Classroom and Zoom. Google Classroom and Zoom were chosen because they are easier for the teacher to implement, besides that the two online applications facilitate coordination and communication between teachers and students, so that the material being taught can be discussed more easily, more freely and can be updated more quickly. In this community service, Online Learning training was conducted for the Elementary School / Madrasah Ibtidaiyah Level with Various Online Applications at MIN 2 Serdang Bedagai. This community service has reached the implementation stage of training which begins with the initial survey stage, provision of training modules and also training. The next stage is monitoring and evaluation and feedback.
Answer Counting Under Guarded TGDs.

We study the complexity of answer counting for ontology-mediated queries and for querying under constraints, considering conjunctive queries and unions thereof (UCQs) as the query language and guarded TGDs as the ontology and constraint language, respectively. Our main result is a classification according to whether answer counting is fixed-parameter tractable (FPT), W[1]-equivalent, #W[1]-equivalent, #W[2]-hard, or #A[2]-equivalent, lifting a recent classification for UCQs without ontologies and constraints due to Dell et al. [Holger Dell et al., 2019]. The classification pertains to various structural measures, namely treewidth, contract treewidth, starsize, and linked matching number. Our results rest on the assumption that the arity of relation symbols is bounded by a constant and, in the case of ontology-mediated querying, that all symbols from the ontology and query can occur in the data (so-called full data schema). We also study the meta-problems for the mentioned structural measures, that is, to decide whether a given ontology-mediated query or constraint-query specification is equivalent to one for which the structural measure is bounded.
Refined Notions of Parameterized Enumeration Kernels with Applications to Matching Cut Enumeration

An enumeration kernel as defined by Creignou et al. [Theory Comput. Syst. 2017] for a parameterized enumeration problem consists of an algorithm that transforms each instance into one whose size is bounded by the parameter plus a solution-lifting algorithm that efficiently enumerates all solutions from the set of the solutions of the kernel. We propose to consider two new versions of enumeration kernels by asking that the solutions of the original instance can be enumerated in polynomial time or with polynomial delay from the kernel solutions. Using the NP-hard Matching Cut problem parameterized by structural parameters such as the vertex cover number or the cyclomatic number of the input graph, we show that the new enumeration kernels present a useful notion of data reduction for enumeration problems which allows to compactly represent the set of feasible solutions.