Reviewerly/exHarmony huggingface dataset:
A Novel Recommendation Algorithm Based on Time-aware

The primary key in novel recommendation is to judge whether an item is "new" for target users.The novelty of recommended items to users changes with time.Therefore, the effects of time factors on user preference, as well as the novelty and accuracy of the recommended items, have become key to novelty recommendation research.To increase novelty based on recommendation accuracy, a novel recommendation algorithm based on time-aware was proposed.First, a time attenuation parameter was introduced into a traditional algorithm to strengthen the weights of recently adopted items.Next, the calculation of innovation adoption activism was adjusted through time information.Differences among recommended items were calculated by combining the time of items that have been adopted by users and the quantity of relevant categories.Lastly, the novelty of recommended items was calculated by the result fusion strategy for Top-N recommendation.Meanwhile, the effectiveness of the proposed algorithm was verified by conducting an offline experiment.Results demonstrate that the novelty of the recommendation list by the proposed algorithm is significantly higher than that of traditional algorithms.Novelty when the number of items in the candidate set reaches 300-400 reaches the peak.The recall rate of unknown items increases by 25%, whereas the recall rate of known items decreases by 60%.The average popularity of the recommendation list decreases by 50%, and the coverage rate increases by 200%.The proposed algorithm improves the ability of the recommendation system (RS) to explore novel items.This study provides references for improving the satisfaction of users with RSs.
MENINGKATAN KEMAMPUAN MENULIS TEKS EKSPOSISI DENGAN MENERAPKAN METODE CUSH & WORD DI KELAS X OTKP SMK NEGERI 1 GUNUNGSITOLI BARAT T.A. 2020/2021

This research is a Classroom Action Research which is carried out to improve and increase the writing skills of students of class X OTKP SMK Negeri 1 Gunungsitoli Barat T.A. 2020/2021 by applying the Cush & Word method. Identification of the problem is a lack of student motivation and a lack of student ability to find ideas in writing. The study was carried out in a single cycle consisting of 5 meetings with 11 students as research subjects. The instruments used were an observation sheets and student ability test sheets. The result of the study proved that the Cush & Word method improved the students' ability in writing exposition text in class X OTKP SMK Negeri 1 Gunungsitoli Barat TA 2020/2021.
Reflecting Back and Looking Forward at &lt;em&gt;JGR: Biogeosciences&lt;/em&gt;

The outgoing and incoming editors in chief of JGR: Biogeosciences reflect on recent years of growth and expansion in the journal while they ponder and plan for the challenges ahead.
Extending Conditional Convolution Structures For Enhancing Multitasking Continual Learning

Conditional operations have received much attention in recent deep learning studies to facilitate the prediction accuracy of a model. A recent advance toward this direction is the conditional parametric convolutions (CondConv), which is proposed to exploit additional capacities provided by the deep model weights to enhance the performance, whereas the computational complexity of the model is much less influenced. CondConv employs input-dependent fusion parameters that can combine multiple columns of convolution kernels adaptively for performance improvement. At runtime, the columns of kernels are on-line combined into a single one, and thus the time complexity is much less than that of employing multiple columns in a convolution layer under the same capacity. Although CondConv is effective for the performance enhancement of a deep model, it is currently applied to individual tasks only. As it has the nice property of adding model weights with computational efficiency, we extend it for multi-task learning, where the tasks are presented incrementally. In this work, we introduce a sequential multi-task (or continual) learning approach based on the CondConv structures, referred to as CondConv-Continual. Experimental results show that the proposed approach is effective for unforgetting continual learning. Compared to current approaches, CondConv is advantageous to offer a regular and easy-to-implement way to enlarge the neural networks for acquiring additional capacity and provides a cross-referencing mechanism for different task models to achieve comparative results.
Development of iOS: A Revolutionary Transformation and the Future

This letter is all about the analysis of first ever operating system that changed the entire era of mobile phones. The study also shows the continuous upgradation of iOS and its amazing contribution in different technologies. We researched how iOS is most secured and different from other operating system with an overview of journey of Apple Inc. The expansion of iOS with its wide scope and the properties which differentiate iOS have also been discussed in our study. Also, we have tried to explore how iOS is upgrading its versions at certain period of time and adding new technologies and features like face recognition, merging software with hardware including the concept of machine learning, how iOS is most secured mobile operating system and what makes it so, what makes iOS different from other mobile operating system like Android and others.
Representations of syntax [MASK] useful: Effects of constituency and dependency structure in recursive LSTMs

Sequence-based neural networks show significant sensitivity to syntactic structure, but they still perform less well on syntactic tasks than tree-based networks. Such tree-based networks can be provided with a constituency parse, a dependency parse, or both. We evaluate which of these two representational schemes more effectively introduces biases for syntactic structure that increase performance on the subject-verb agreement prediction task. We find that a constituency-based network generalizes more robustly than a dependency-based one, and that combining the two types of structure does not yield further improvement. Finally, we show that the syntactic robustness of sequential models can be substantially improved by fine-tuning on a small amount of constructed data, suggesting that data augmentation is a viable alternative to explicit constituency structure for imparting the syntactic biases that sequential models are lacking.
Joint Nonlocal Low Rank and High-order Total Variation Priors for Multiplicative Noise Removal

In this paper, we propose an effective variational model for multiplicative noise removal problem with joint nonlocal low rank and high-order total variation (HTV) priors. Specifically, the proposed model fully exploits the good capacity of reducing staircase effect of HTV prior as well as the good capacity of preserving structures and textures of nonlocal low rank prior. Moreover, an alternating minimization algorithm is designed to solve the proposed model. Finally, the experiments show that the proposed method performs better image quality than the compared methods for multiplicative noise removal problem.
FILLING THE CONTENT AND ACTIVITY COMPONENTS OF THE TRAINING METHOD OF STUDENTS OF PEDAGOGICAL SPECIALTIES FOR FORMATION IN TRAINING COMPETENCES OF DIGITAL ECONOMY

The restructuring of the modern education system is aimed at implementing plans to provide personnel for the digital economy, creating a focus on the specialties of the digital economy for schools and secondary vocational educational institutions, and, accordingly, transforming the system of training students in pedagogical universities in meaningful, methodological and technological aspects. Therefore, the research problem is associated with the development of a methodology for preparing students of pedagogical specialties for the formation, development and diagnostics of the development of the components of the competencies of the digital economy in students. The purpose of the described study: to develop a variant of filling the content-active components of the methodology for preparing students of pedagogical specialties for the formation, development and diagnosis of development of the components of competencies of the digital economy in students. Based on the analysis of literary sources, regulatory documents, expert materials, generalization of research results, structural modeling, the option of filling the content-activity components of the methodology for preparing students of pedagogical specialties to form, develop and diagnose the development of competency components in students is established and the types of activities are found to correspond to the competencies of the digital economy. The practical and theoretical significance of the results lies in the development of an indicative base for designing a methodology for the formation of competencies of the digital economy. Conclusions are formulated on the feasibility of considering subject content and the content of activities with content as a whole; the feasibility of establishing compliance of the content of the content-activity components of the methodology for preparing students to the components of the competencies of the digital economy and conducting compliance based on the analysis of the nature of the types of activities and the allocation of the operational composition of actions; the need for self-testing of innovative substantive and methodological solutions.
KEEFEKTIFAN KALIMAT DALAM TEKS LAPORAN HASIL OBSERVASI KARYA SISWA KELAS VII SMP NEGERI 17 PADANG

ABSTRACT The purpose of this research was to describe the effectiveness of the sentence in text of results of observations report of Grade VII students of SMP Negeri 17 Padang. The effectiveness is reviewed based on four indicators, namely (1) right reasoning, (2) right linguistic, (3) right words and terms, and (4) right spelling. This type of research is a qualitative research with descriptive methods. This research was carried out at Padang 17 Public Middle School, which was considered important because it was an attempt to explore and understand the effectiveness of student sentences. The instrument in this study is the researcher himself. The results of this study revealed that the effectiveness of the text sentences from students' observations report can be said to be ineffective, it is 39 or 14% of 281 sentences. Based on the results of research on the effectiveness of the sentence in the text of observation report, it can be concluded into four things. First, students still commit errors in proper reasoning, namely regarding lodic ideas and unity of ideas. Secondly, students still commit errors in proper linguistics, namely regarding (1) placement of elements of syntactic functions correctly, (2) no missing sentence elements, (3) no redundant sentence elements, and (4) parallel sentence elements. Third, students still commit errors in the proper words and terms, namely regarding (1) right concepts, (2) right sense scores, (3) right collocation, and (4) right usage context. Fourth, students still commit errors in correct spelling, namely regarding (1) the use of letters, (2) writing words, and (3) use of punctuation . Kata kunci: Keefektifan Kalimat, Teks Laporan Hasil Observasi
InteractML: Node Based Tool to Empower Artists and Dancers in using Interactive Machine Learning for Designing Movement Interaction

Artists, and particularly dancers, have a deep knowledge of movement that can inform movement interaction for immersive media. There are few tools that enable movement interaction in immersive technology to be easily designed by nonprogrammers. We explain the design rationale behind InteractML a node-based interactive machine learning tool for designing movement-based interaction. We also describe a design principle:that the design should serve a dual purpose of increasing understanding of machine learning whilst giving access to customise the machine learning system.
Explainable Reinforcement Learning Through Goal-Based Interpretability

Deep Reinforcement Learning agents achieve state-of-the-art performance in many tasks at the cost of making them black-boxes, hard to interpret and understand, making their use difficult in trusted applications, such as robotics or industrial applications. We introduce goal-based interpretability, where the agent produces goals which show the reason for its current actions (reach the current goal) and future goals indicate its desired future behavior without having to run the environment, a useful property in environments with no simulator. Additionally, in many environments, the goals can be visualized to make them easier to understand for non-experts. To have a goal-producing agent without requiring domain knowledge, we use 2-layer hierarchical agents where the top layer produces goals and the bottom layer attempts to reach those goals. Most classical reinforcement learning algorithms cannot be used train goal-producing hierarchical agents. We introduce a new algorithm to train these more interpretable agents, called HAC-General with Teacher, an extension of the Hindsight Actor-Critic (HAC) algorithm that adds 2 key improvements: (1) the goals now consist of a state s to be reached and a reward r to be collected, making it possible for the goal-producing policy to incentivize the goal-reaching policy to go through high-reward paths and (2) an expert teacher is leveraged to improve the training of the hierarchical agent, in a process similar but distinct to imitation learning and distillation. Contrarily to HAC, there is no requirement that environments need to provide the desired end state. Additionally, our experiments show that it has better performance and learns faster than HAC, and can solve environments that HAC fails to solve.
PERSONALITY-ORIENTED APPROACH TO THE EDUCATION OF MEANINGFUL LIFE VALUES IN ADOLESCENTS

The article covers the application of apersonality-oriented approach to theeducation of meaningful life values inadolescents. It is proved that thepersonality-oriented approach reflectsthe patterns of development and thespecifics of educating the meaning oflife values of adolescents, where themain priority is given to the realizationof the essential forces of the child andopposes treatment as a means oftechnocracy, unification and leveling ofpersonality. Personality-orientedapproach ensures the right of thegrowing individual to choose one oranother meaningful life values, todevelop their own values and behavior.An important aspect of applying aperson-centered approach is therejection of moralism, formalism andschematicism in the education ofmeaningful life values in adolescents.The application of a person-centeredapproach to the education ofmeaningful life values in childrenthrough an emotionally positiveattitude, respect for dignity implies theinterest and involvement of all subjectsof the educational process. Conditionsfor effective application of personalityorientedapproach were: creating anappropriate educational environment,using various forms and methods ofindividual work with children(psychological and pedagogicalsupport, individual conversations,consultations, pedagogicalobservation, project work, preparationfor educational activities, competition),self-education, etc. The effectivenessof the use of different types of reflection(regulatory, determining, synthesizing,creative) and the reflexive-explicitmethod of I. Bekh, subject-to-subjectinteraction is confirmed.
COVID-19 data on the fringes: the Scottish story

COVID-19 hit at a time when the United Kingdom was vulnerable, reeling from its exit from the European Union and wracked by ongoing issues over the devolved nations, particularly Northern Ireland and Scotland, both of which had voted to remain in the EU during the 2016 referendum. Scotland had its own 2014 referendum on independence from the UK, which was narrowly won by the “No” side. While a pro-Brexit, right-wing Conservative government rules in London, the devolved administration in Edinburgh is led by the center-left Scottish National Party (SNP) government and first minister Nicola Sturgeon. However, when the pandemic first hit the UK in the early months of 2020, there was no discernible difference in approach between the Scottish government and the UK Government. In March 2020, both Scotland and the wider UK imposed lockdowns later than in other European countries. By mid-March, both had abandoned manual contact tracing around the same time that “Big Tech” firms such as Palantir were invited to meetings with the UK government. Later that month, NHSX (the English public health service unit tasked with setting policy and best practice for digital technologies and data in health) started developing a contact-tracing app amid techno-deterministic claims from the Johnson administration in London that we could digitize our way out of the pandemic.
Students Problem in Speaking English at Second Semester. (A Qualitative research on the English Education Department of State Islamic University Sultan Maulana Hasanuddin Banten)

Ratna Sari. 161230115. 2020. Students Problem in Speaking English at Second Semester. (A Qualitative research on the English Education Department of State Islamic University Sultan Maulana Hasanuddin Banten).This research investigates the students problem in speaking English. The purpose of the research is to find out the difficulties, the factors and the way to overcome those problem. This research uses a survey through questionnaire, interview and observation through lecturer. Based on the result there are 70 respondents has filled in and given the answer on questionnaires. There are 62,9% or 44 respondents choose Agree that they feel difficult in speaking English. The result found some difficulties faced by students in speaking English they are vocabularies, grammar and pronounciation. Almost students choose vocabulary become one problem that make difficult in speaking English. Although many students also choose other factors, but lack of vocabulary become the big problem for them. All students also have some wishes to improve their skill especially in speaking English so almost students choose want to be more practice.
Optimal tests for continuous-variable quantum teleportation and photodetectors

Quantum teleportation is a primitive in several important applications, including quantum communication, quantum computation, error correction, and quantum networks. In this work, we propose an optimal test for the performance of continuous-variable (CV) quantum teleportation in terms of the energy-constrained channel fidelity between ideal CV teleportation and its experimental implementation. All work prior to ours considered suboptimal tests of the performance of CV teleportation, focusing instead on its performance for particular states, such as ensembles of coherent states, squeezed states, cat states, etc. Here we prove that the optimal state for testing CV teleportation is an entangled superposition of twin-Fock states. We establish this result by reducing the problem of estimating the energy-constrained channel fidelity between ideal CV teleportation and its experimental approximation to a quadratic program and solving it. As an additional result, we obtain an analytical solution to the energy-constrained diamond distance between a photodetector and its experimental approximation. These results are relevant for experiments that make use of CV teleportation and photodetectors.
Consistent Instance Classification for Unsupervised Representation Learning

In this paper, we address the problem of learning the representations from images without human annotations. We study the instance classification solution, which regards each instance as a category, and improve the optimization and feature quality. The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance. The benefit of optimizing the consistency loss is that the learned features for augmented views from the same instance are more compact and accordingly the classification loss optimization becomes easier, thus boosting the quality of the learned representations. This differs from InstDisc and MoCo that use an estimated prototype as the classifier weight to ease the optimization. Different from SimCLR that directly compares different instances, our approach does not require large batch size. Experimental results demonstrate competitive performance for linear evaluation and better performance than InstDisc, MoCo and SimCLR at downstream tasks, such as detection and segmentation, as well as competitive or superior performance compared to other methods with stronger training setting.
Применение МООК в учебном процессе высших учебных заведений

The article studies the main theoretical and practical provisions on the structural application of MOOCs in higher education. The main barriers towards the use of MOOCs in Russian universities are considered as well.
О работе над эскизом и натурным материалом к композиции

The article presents the principles of a professional approach to the execution of a preliminary sketch and usage of life studies in educational and creative painting. At the crux of the analysis lie objective complications that professional artists and students of art schools encounter while transforming a small-sized sketch into a large format, as well as potential negative consequences of unnecessarily detailed composition design. It delineates major tasks (search for the composition layout, location of its center and general masses, scale ratios) that are to be fulfilled under an optimally structured preparation of the sketch. Fine art masterpieces are employed to illustrate the principles of developing life study material for the execution of the conceived painting project, as well as changes in work techniques (from life drawings to full-color life-based painting sketches), which are entailed by the change of artists’ goals and objectives in the creation of paintings pertinent to various periods of the classical European school of fine arts. Particular attention is paid to the prioritization of the idea and artistic imagery over the life study material, combined with the need to cultivate artist’s ability to visualize the composed work of fine art and successfully implement this visualization in the intended format of the painting, while preserving valuable emotional components of the artist’s work.
Multi Center Polyhedral Conic Classifiers for Estimating Non-Linear Decision Boundaries

Polyhedral conic classifiers are getting popular with the performance against support vector machines (SVM). In these classifiers a conic function with a vertex point is used. Vertex point is an important parameter and improves the performance when it is set to the mean of positive samples. In cases where positive data belonging to the same class are clustered in different regions, a single classifier is not enough, and more than one classifier is needed. In this study, a novel multi center polyhedral conic classifiers (MCPCC) method is developed to use only one classifier to classify positive data clustered in different centers. Experiments are performed on two synthetic datasets using proposed method. The proposed method was compared with Kernel SVM and other polyhedral conic classifiers and it was found to give impressive results.