Reviewerly/exHarmony huggingface dataset:
ARTISTIC IMAGERY AS PHENOMENON CREATION ANALITICAL MEANS IN DIFFERENT KINDS OF ARTS

The article treats artistic imagery from the point of view of its ability to analyse phenomenon of creation. The author states that not only scientific categories can analyse it but artistic image also can do it none the worse, and discover such aspects in the essence of phenomenon of creation that science did not consider yet. It is proved by analyses of XIX-XX century works of worldwide authors in different spheres of artistic creation: literature in prose and poem forms (Pushkin, Joyce, Sarraute, Tsvetaeva); dramaturgy (Tchechov, Bekket, Ionesco); cinema ( Fellini, Tarkovsky); painting (Picasso).The author finds links between their images using such analytical methods as structural, semiotics and comparison ones, and author’s method of metatextual invariability. It makes possible to find fundamental structural elements as invariants of analysed artistic systems. Each of them develops in his own way invariants’ semantics concerning creation phenomenology notably that of artist’s seeking his individual language as expression of his reality subjective viewing. What kind of phenomena happen then in the artist’s soul can discover only artistic imagery means; scientific categories are powerless of doing it.. The author points out at the whole set of symbols which are used as phenomenon creation analytical means in artistic discourse.
Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs

OpenStreetMap (OSM) is one of the richest openly available sources of volunteered geographic information. Although OSM includes various geographical entities, their descriptions are highly heterogeneous, incomplete, and do not follow any well-defined ontology. Knowledge graphs can potentially provide valuable semantic information to enrich OSM entities. However, interlinking OSM entities with knowledge graphs is inherently difficult due to the large, heterogeneous, ambiguous, and flat OSM schema and the annotation sparsity. This paper tackles the alignment of OSM tags with the corresponding knowledge graph classes holistically by jointly considering the schema and instance layers. We propose a novel neural architecture that capitalizes upon a shared latent space for tag-to-class alignment created using linked entities in OSM and knowledge graphs. Our experiments performed to align OSM datasets for several countries with two of the most prominent openly available knowledge graphs, namely, Wikidata and DBpedia, demonstrate that the proposed approach outperforms the state-of-the-art schema alignment baselines by up to 53 percentage points in terms of F1-score. The resulting alignment facilitates new semantic annotations for over 10 million OSM entities worldwide, which is more than a 400% increase compared to the existing semantic annotations in OSM.
LibGuides: TigerHow: Set Up Email, Blackboard and Banner Web (Distance Education): PATH 3: Accessing Email/Office 365

Learn how to set up and log into email, Blackboard and Banner Web. Accessing Email/Office 365
Evaluation of contextual embeddings on less-resourced languages

The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives. Most existing work focuses on English; in contrast, we present here the first multilingual empirical comparison of two ELMo and several monolingual and multilingual BERT models using 14 tasks in nine languages. In monolingual settings, our analysis shows that monolingual BERT models generally dominate, with a few exceptions such as the dependency parsing task, where they are not competitive with ELMo models trained on large corpora. In cross-lingual settings, BERT models trained on only a few languages mostly do best, closely followed by massively multilingual BERT models.
NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search

Most existing neural architecture search (NAS) benchmarks and algorithms prioritize performance on well-studied tasks, e.g., image classification on CIFAR and ImageNet. This makes the applicability of NAS approaches in more diverse areas inadequately understood. In this paper, we present NAS-Bench-360, a benchmark suite for evaluating state-of-the-art NAS methods for convolutional neural networks (CNNs). To construct it, we curate a collection of ten tasks spanning a diverse array of application domains, dataset sizes, problem dimensionalities, and learning objectives. By carefully selecting tasks that can both interoperate with modern CNN-based search methods but that are also far-afield from their original development domain, we can use NAS-Bench-360 to investigate the following central question: do existing state-of-the-art NAS methods perform well on diverse tasks? Our experiments show that a modern NAS procedure designed for image classification can indeed find good architectures for tasks with other dimensionalities and learning objectives; however, the same method struggles against more task-specific methods and performs catastrophically poorly on classification in non-vision domains. The case for NAS robustness becomes even more dire in a resource-constrained setting, where a recent NAS method provides little-to-no benefit over much simpler baselines. These results demonstrate the need for a benchmark such as NAS-Bench-360 to help develop NAS approaches that work well on a variety of tasks, a crucial component of a truly robust and automated pipeline. We conclude with a demonstration of the kind of future research our suite of tasks will enable. All data and code is made publicly available.
INTELLECTUAL PROPERTY EDUCATION AS THE ETHICAL DIMENSION OF INFORMATION LITERACY: DISCIPLINARY ISSUES

C. Basili 1, T. Trencheva 2, T. Kiryakova-Dineva 3 1University Guglielmo Marconi – National Research Council - Rome (ITALY) 2University of Library Studies and Information Technologies (BULGARIA) 3South-West University Neofit Rilski (BULGARIA)
DIGITAL COMPETENCE OF TEACHERS AS A CURRENT NECESSITY IN EDUCATION

S. Treľová1, S. Krásna2 1Comenius University in Bratislava, Faculty of Management (SLOVAKIA) 2DTI University (SLOVAKIA)
The Influence of Narrow Reading on Reading Comprehension of the Eleventh Grade Students of SMAN 1 Srono in 2018/2019 Academic Year

The use of approach or strategy in teaching and learning process was needed to help EFL students in developing their four skills in English. One of the skills that needs to be developed is reading because reading activities include many things, one of which is reading comprehension. The aim of the research was to find the significant influence of narrow reading on reading comprehension of elevent grade students of SMAN 1 Srono in 2018/2019 academic year. The research design was experimental research and the researcher used one group pre-test and post-test design. Before giving a treatment, the respondents were given a pretest. Then the treatment was carried out for 3 meetings. After that the respondents were given test again as post-test. The researcher used purposive sampling to determine the research area. The reasearch area was SMAN 1 Srono. There were 35 students of elevent science 3 as respondents. It was taken by purposive sample. The data was obtained by using multiple choice test. There were 20 questions about topics, moral value, the characteristics, plot of story, the purpose of narrative text and vocabulary, finding synonim and references. The results of T-test showed that there was significant influence on students’ reading comprehension abilities. The value of T-test was 6,674, while the value of T table at the significant level of 5% and degree of freedom 33 was 2,012. It means that the value of T-tes was higher than the value of T table (6,674 &gt; 2,012). Therefore it can be concluded that the narrow reading had significant influence on reading comprehension of elevent grade students of SMAN 1 Srono in 2018/2019 academic year. Based on the results of T-test, the researcher suggested to the English teacher to apply the narrow reading approach in the reading class to make students are more interested and enjoyable to study. The other reseracher are suggested to do the same research with different grade students, school and research design and use the other genres. It is recommended to make experimental research using two group, experimental group and control group.
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning

Creating representations of shapes that are invari-ant to isometric or almost-isometric transforma-tions has long been an area of interest in shape anal-ysis, since enforcing invariance allows the learningof more effective and robust shape representations.Most existing invariant shape representations arehandcrafted, and previous work on learning shaperepresentations do not focus on producing invariantrepresentations. To solve the problem of learningunsupervised invariant shape representations, weuse contrastive learning, which produces discrimi-native representations through learning invarianceto user-specified data augmentations. To producerepresentations that are specifically isometry andalmost-isometry invariant, we propose new dataaugmentations that randomly sample these transfor-mations. We show experimentally that our methodoutperforms previous unsupervised learning ap-proaches in both effectiveness and robustness.
SISTEM PAKAR DIAGNOSIS PENYAKIT KULIT PADA KUCING BERBASISI WEB DENGAN METODE FORWARD CHAINING

One of the most popular pets to keep is a cat. Cats are one of the animals that are kept by the community because they can be good friends for humans. To help the general public, especially cat keepers whose knowledge is still lacking in recognizing the types of skin diseases in cats and save time and costs in solving problems by using a system expert. An expert system that will be used to diagnose skin diseases in cats, as well as provide ways to prevent and overcome them using the forward chaining method. Forward Chaining is an information-driven (data-driven) approach. In this approach, the search begins with input data, and then attempts to describe conclusions. Forward tracking looks for facts that match the IF part of the IF-THEN condition. then the rule is stimulated and the next rule is tested. The results of this study are a system that can diagnose skin diseases in cats based on a website. With the existence of an expert system for diagnosing skin diseases in cats, it can help the general public, especially cat keepers, to find out earlier about the types of skin diseases in cats and the symptoms of these skin diseases. and handling solutions.
WEB-BASED APPLICATION FOR REGISTRATION AND MANAGEMENT OF ‘HAK KEKAYAAN INTELEKTUAL’ IN THE LRPM DEPARTMENT PRESIDENT UNIVERSITY

&lt;p&gt;&lt;strong&gt;Copyright is needed by everyone. They can file a copyright for a work they own to the government. If the submitted work has received a certificate of ownership rights to the work, then every person copying the work that is already certified will get a fine as regulated in the Undang – Undang Nomer 28 Tahun 2004 on copyright. President University has a department that manages and processes copyrights submitted by lecturers, employees or students on campus. In the process, currently only using a manual process, namely by face to face to submit a copyright application form. Not only that, the LRPM, the copyright filing manager, still uses the storage system on his computer and this makes his computer easy to experience problems because too much data is stored. From here an idea is created that can help make the process run fast, doesn't take a long time and reduces the problems that exist in storing approved forms. This application can also help creator to see the progress of copyright submissions digitally and download e-certificate files digitally, as well as fill out submission forms digitally. This can also serve to reduce the use of existing paper. For LRPM, they can easily see the neatly arranged storage according to the time of submission without downloading the scanned form. Process form submissions quickly and can upload e-certificates in the submitting platform.&lt;/strong&gt;&lt;/p&gt;
THE REALITY OF ARTIFICIAL INTELLIGENCE: SOCIAL LEARNING THEORY, CONNECTIVISM, AND HUMAN STANDARDS IN LEARNING VERSUS MACHINE LEARNING

THE REALITY OF ARTIFICIAL INTELLIGENCE: SOCIAL LEARNING THEORY, CONNECTIVISM, AND HUMAN STANDARDS IN LEARNING VERSUS MACHINE LEARNING
PACKAGING INFORMATION OR INFORMATION PACKAGING: THE BULGARIAN EXPERIENCE OF GOOD EDUCATIONAL PRACTICES AT THE UNIVERSITY LEVEL

PACKAGING INFORMATION OR INFORMATION PACKAGING: THE BULGARIAN EXPERIENCE OF GOOD EDUCATIONAL PRACTICES AT THE UNIVERSITY LEVEL
Implementasi Aplikasi Monitoring Nilai dan Kegiatan Siswa Berbasis Android dengan Metode Prototype

The number of smartphone and internet users in Indonesia is currently very large. This is become the basis for the use and development of mobile-based applications for the advantage of education. However, not all High Schools in Indonesia have a mobile-based system. Another problem is that most of them still use conventional methods in implementing teaching and learning activities, and reporting learning outcomes to parents. This is also still the case at SMA Negeri 1 Pangkalanbaru, Bangka Tengah. This problem is what we want to raise and solve by developing applications that can solve these problems. The research was developed using the Prototype method which consists of stages of Data Collection, Rapid Planning, Prototype Design, and Prototype Testing. By using the UML tool, results are obtained in the form of parents who can monitor grades, school information, and school announcements. In addition, students can take attendance online, register for extracurricular activities, and view announcements. From the testing results it is known that the system performance is running well as it should.
Federated Survival Analysis with Discrete-Time Cox Models

Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy. However, the prominent Cox proportional hazards (PH) model, used for survival analysis, does not fit the FL framework, as its loss function is non-separable with respect to the samples. The naive method to bypass this non-separability consists in calculating the losses per center, and minimizing their sum as an approximation of the true loss. We show that the resulting model may suffer from important performance loss in some adverse settings. Instead, we leverage the discrete-time extension of the Cox PH model to formulate survival analysis as a classification problem with a separable loss function. Using this approach, we train survival models using standard FL techniques on synthetic data, as well as real-world datasets from The Cancer Genome Atlas (TCGA), showing similar performance to a Cox PH model trained on aggregated data. Compared to previous works, the proposed method is more communication-efficient, more generic, and more amenable to using privacy-preserving techniques.
Quantum Logspace Algorithm for Powering Matrices with Bounded Norm.

We give a quantum logspace algorithm for powering contraction matrices, that is, matrices with spectral norm at most~1. The algorithm gets as an input an arbitrary $n\times n$ contraction matrix $A$, and a parameter $T \leq \mathrm{poly}(n)$ and outputs the entries of $A^T$, up to (arbitrary) polynomially small additive error. The algorithm applies only unitary operators, without intermediate measurements. We show various implications and applications of this result: First, we use this algorithm to show that the class of quantum logspace algorithms with only quantum memory and with intermediate measurements is equivalent to the class of quantum logspace algorithms with only quantum memory without intermediate measurements. This shows that the deferred-measurement principle, a fundamental principle of quantum computing, applies also for quantum logspace algorithms (without classical memory). More generally, we give a quantum algorithm with space $O(S + \log T)$ that takes as an input the description of a quantum algorithm with quantum space $S$ and time $T$, with intermediate measurements (without classical memory), and simulates it unitarily with polynomially small error, without intermediate measurements. Since unitary transformations are reversible (while measurements are irreversible) an interesting aspect of this result is that it shows that any quantum logspace algorithm (without classical memory) can be simulated by a reversible quantum logspace algorithm. This proves a quantum analogue of the result of Lange, McKenzie and Tapp that deterministic logspace is equal to reversible logspace [LMT00]. Finally, we use our results to show non-trivial classical simulations of quantum logspace learning algorithms.
DISTRACTION (GANGGUAN) DALAM PEMBELAJARAN ONLINE

Changes in the teaching and learning process, which initially took place face-to-face learning are now carried out by online learning, experienced several obstacles or disturbances ranging from inadequate internet quotas, unstable networks to lack of students' understanding of the material presented by the teacher. Online learning is one of the ways recommended by the government to maximize the quality of learning during the Covid-19 pandemic. The results of the study show that the disruption in online learning, among others, is that first, students do not focus on learning due to boredom and boredom in students so that the teacher must implement strategies that make the learning atmosphere happier. Second, there are inadequate online learning facilities because not all students have online learning support devices, and there are also areas that are not covered by the internet. Third, there is no readiness of teachers and parents of students for online learning, where there are still teachers who do not have the competence to teach online and also parents have their own activities, so they cannot accompany students when online learning takes place.
Artificial Intelligence Based College Enquiry Chatbot

A chat bot is supposed to start a conversation between humans and machines. The machine has integrated the knowledge to identify sentences and make a decision itself in response to a question. Chat bots will be entirely text-based. User interface through which the user can enter commands and receive text replies. Chatbots are usually services that remember previous commands to provide functionality. It can be used safely by an even larger audience when using chatbot technology. Integrated with popular web services. University research chatbots are created using artificial algorithms that analyze user requests and understand the user's message. The answer principle is to match a user's input sentence via the chat bot, without being physically available to the university for inquiries. The system analyzes the question and then answers the user. The system responds to the students' inquiries with its intelligence.
A Systematic Review Comparing Different Security Measures Adopted in Automated Teller Machine

The automated teller machines (ATM) uses are increasing day by day. Majority of the population uses ATM cards for shopping and cash withdrawal from ATMs. Along with increasing use of ATM cards the numbers of ATM thefts are also increasing worldwide. Majority of the ATM thefts are by manipulating or cloning the card. The incidences of physical ATM attacks by breaking into the ATM are also increasing worldwide. The securities of the customers are important in ATM transactions. The customers are protected from both physical and fraudulent ATM attacks by enhanced security measures   provided by the bank. Majority of the ATMs provide fraud attacks by using a valid ATM card and PIN which could be easily identified and manipulated resulting in thefts. This paper discusses about the enhanced security measures implemented like face recognition, fingerprint identification, sensors to detects physical attacks along with PIN in various literatures. The merits and demerits of each security systems are compared.
Automatic-Subtitling: Comparison on the Performance of Forced Alignment and Automatic Speech Recognition

This work is focusing on the automatic generation of subtitles using different tools that can be categorized as Forced Aligners (FAs) or Automatic Speech Recognizers (ASRs). A comparison of the performance of FA and ASR for the task of generating same-language subtitles was conducted. The prime motivation was a previous task, which was the extraction of sentence-utterances in different audio files using word-timestamps. Three different tools were used for this work: aeneas [1] which is an FA, Cerence [2], which is an ASR and Sonix [3], which is also an ASR. We conducted a technical evaluation and a subjective evaluation based on a case study. In this study people were presented with different stimuli, each stimulus using generated subtitles based on the time-information given by the different tools mentioned above. The resulting data of a case study confirmed a rise in performance of Cerence compared to aeneas.
AUGMENTED REALITY TO TEACH GEOMETRY: A PROPOSED TASK IN LOCKDOWN CONTEXT

Over the past few years, the teaching and learning of mathematics have undergone several changes, not only in terms of content, but mainly in terms of objectives and methodologies. Currently, it is intended that students participate in numerous and varied experiences that stimulate their taste and pleasure for mathematics, that encourage them to conjecture, explore and learn from mistakes. Allied to these changes is also the use of new technologies in mathematics education.Geometry is one of the areas of mathematics where the use of technologies is an asset, since it influences the way geometry is taught and learned. Throughout elementary school, students are expected to develop the notions of spatial sense and visualization, which are considered essential aspects in the teaching of geometry. Spatial visualization involves the ability to imagine the movement of objects and spatial forms, such as the construction and manipulation of mental representations of two- and three-dimensional objects and the perception of an object from different perspectives. In the essential learning defined for the subject of mathematics it is stated that students should develop visualization skills through concrete experiences with a diversity of geometric objects and with technologies, rotating, turning, sliding, shrinking and deforming two- and three-dimensional objects. Technological tools provide access to powerful visual models. In this way, technology enriches the extent and quality of research in geometry by providing a means of visualizing geometric notions from different perspectives. In this sense, the use of augmented reality in the teaching and learning of mathematics is a very important tool, because if well applied it promotes the development of numerous skills by students, making them more autonomous and builders of their own knowledge. Augmented reality environments allow students to interact with the real and virtual worlds, exploring objects, performing tasks, learning concepts, and developing skills.The purpose of this article is to present a proposal for a task, based on the Metaverse augmented reality program that can be applied in 6th grade mathematics classes of the 2nd cycle of basic education (In the Portuguese education system in the 2nd cycle of basic education the students have11 to 12 years-old) in the unit "Geometric solids. Volumes", in which several contents are covered, such as the classification of solids into polyhedra and non-polyhedra; classification of prisms and pyramids, as well as their properties; the planification of solids and the calculation of volumes. The development of this task is due to the fact that most students feel difficulties in the theme of spatial geometry, that is, in the visualization and interpretation of spatial geometric shapes.