Reviewerly/exHarmony huggingface dataset:
KONTRIBUSI KETERAMPILAN MENYIMAK PANTUN TERHADAP KETERAMPILAN MENULIS PANTUN SISWA KELAS VII SMP NEGERI 34 PADANG

ABSTRACT This article is written to discuss the results of research on contribution of skills listening rhyme to writing skills rhyme grade strudents VII SMP Negeri 34 Padang. The purpose of this research there are three. First, describe the skills of listening rhyme. Second, describe the skils of writing rhyme. Third, describe the contributing skills of listening rhyme for the writing skill of the rhyme grade students VII SMP Negeri 34 Padang. This research type is quantitative descriptive method. The design used in this research is the design contribution. This research population is grade VII SMP Negeri 34 Padang are registered on the school year 2018/2019, i.e. as many as 277 students. The sample in this research are determined by proportional random sampling of 15% of the population, i.e. 45 students. Research data is a score test results intermediate listening rhyme and score the writing skills test result text rhyme. The data obtained through two types of tests i.e. objective tests to measure the skills of listening rhyme and performance tests to measure the skills of writing a text rhyme. The results of the research there are three. First, the skills of listening rhyme grade VII SMP Negeri 34 Padang are on good qualifications (B) by the value of the average 77,67. Second, the skills of writing rhyme grade VII SMP Negeri 34 Padang are on good qualifications (B) with an average of 82,31. Third, there is a contribution of listening skills rhyme to the skills of writing rhyme of class grade VII SMP Negeri 34 Padang at 57,15% Kata kunci: Kontribusi, Menyimak, Menulis, dan Pantun
Highly Efficient Anonymous IoT Authentication using Composite Hashing

Considering many Internet of Things (IoT) devices are resource-limited and their identities might disclose sensitive information, highly efficient and anonymous IoT authentication scheme is desirable for these IoT scenarios. In this paper, we propose a new anonymous IoT authentication scheme, using the composite hashing. The merits of this work are four-fold. (1) A new anonymous IoT authentication scheme is proposed; (2) the pre-calculation of pseudonym vectors can tackle Denial-of-Service attacks or unreliable connection issues; (3) the evaluations and analysis demonstrate its excellent performance in terms of computation, communication, and security properties; (4) an instantiation of applying this scheme on a standard IOT protocol like MQTT is described. Its excellent performances make it very attractive for those resource-limited IoT devices with anonymity requirement.
Социально-психологическая адаптация студентов к обучению в педагогическом вузе

Introduction. This paper presents the results of the ascertaining stage of the experimental study and aims to identify the level of first-year students’ adjustment to studying at the pedagogical university and the characteristics of successful social-psychological adjustment among first-year students studying at the pedagogical university. The authors identified the components of social-psychological adjustment (success in educational activities, success in communication) and the criteria for assessing the level of adjustment in first-year students, including their awareness of the new role of a student, a future teacher (manifests itself through educational performance) and interaction with fellow students and teachers (manifests itself through a student’s individual behavioral style, the ability to express his/her opinion and take into account the opinions of others; the ability to support views and interests of his/her fellow students). Theoretical Basis. The article presents a retrospective view of the phenomenon of adjustment, a comparative analysis of theoretical views on understanding the essence of the concept of ‘adjustment’. This study is the first to clarify the concept of social and psychological adjustment among students of the pedagogical university, taking into account the process of communication of first-year students with their teachers and fellow students and the presence of a future teacher’s individual behavioral style. Results. The findings indicate the presence of high and average levels of adjustment to studying at the pedagogical university among first-year students, which enabled authors to formulate recommendations for organizing educational activities with first-year students at the pedagogical university to more rapidly coordinate their behavior in accordance with the requirements of the university. Discussion. First-year students showed a high level of adjustment to the educational group and an average level of adjustment to educational activities. Consequently, in order to overcome difficulties in mastering certain academic disciplines, the educational environment should be favorable for self-evaluation of educational performance, asking questions for introspection, and developing the ability to self-organize individual activities. Conclusion. The findings of this study are of direct practical relevance in the work of teachers with first-year students and may help choose the most effective forms of work, including interactive ones, contributing to successful adjustment to educational activities.
A Trigger Model of Individual Perception of Information. Transition from Civilization of Word to Civilization of Number and Digit

The purpose of the article is to work out an informational model of an individual that describes their way of perceiving and processing information. The authors use the scholarly apparatus of engineering sciences. The article determines four levels of individual perception of information, namely, physical, emotional, intellectual and axiological. The process of recognizing external signals starts at the physical level, and normally is to run with immediate involvement of the axiological level. The model of individual perception of information is presented as a trigger capable of receiving external physical signals, recognizing them, and transforming them into information that is further broadcast to the environment via physical channels of perception. Distinguishing a signal among background noise is the first stage of individual perception of information. Processing this signal involves activating the emotional and/or intellectual levels of the consciousness, which, in their turn, can lead to long-term changes in the individual’s mind at the axiological level. The proposed model is illustrated by the example of Overtone windows, and is applied in studying causes and consequences of transmission from civilization of the word to civilization of the number and the digit. The authors state that signals and information may be used as tools of manipulating an individual if they constantly and deliberately overload the channels of perception, and eventually block the axiological level in the individual’s mind. The authors substantiate that transition form civilization of the word to civilization of the number and the digit will inevitably lead to degradation of the individual’s abilities to perceive and process information, which will result in cardinal changes in all spheres of social life. The results of the research may be useful for refining the theory of the mass media and optimizing their activities.
Research on the Construction of the AOL of Statistics Course Based on OBE Concept

This paper aims to build an endogenous assurance of learning (AOL) of statistics course based on OBE concept and AOL framework, which is "outcome-oriented, student-centered and quality continuously improving".In the concrete construction, it is mainly realized through the following ways: first, the diversified, multi-stage and multi-level training objectives should be determined based on the talent demand survey.Second, individualized and flexible training programs should be inversely designed based on learning objectives.Third, in order to improve the teaching quality, teaching methods need to be innovated continuously.Fourth, internet technology should be utilized to enrich teaching resources.Fifth, a pluralistic, multi-stage and diversified assessment system should be established.At the same time, the training objectives, training programs, assessment and evaluation system should be constantly improved through the feedback mechanism.
A Novel Verification scheme for Resisting Password Guessing Attacks

User name and password are one of the most commonly used authentication mechanisms in information systems and social networks. Strong passwords are secure, but not easy to memorize; users may choose passwords that are easy to remember as well as easy to be compromised. Therefore, online password guessing attacks becomes a major security threat in information systems and social networks. It is a challenge to provide a reliable user authentication solution that allows legitimate access and prevents password guessing attacks. Our preliminary study observed the fact that legal users know what passwords they have chosen, while attackers can only guess what they are. The proposed solution applies information theory and compares the entropy discrepancy between the passwords entered by the user and attacker. The password entropy is calculated by accumulating the frequencies of the entered characters, not the password itself. The experimental results show that, even if the user selects a common password, the proposed authentication method can distinguish between legitimate users and attackers effectively and efficiently.
Adaptability Preserving Domain Decomposition for Stabilizing Sim2Real Reinforcement Learning

In sim-to-real transfer of Reinforcement Learning (RL) policies for robot tasks, Domain Randomization (DR) is a widely used technique for improving adaptability. However, in DR there is a conflict between adaptability and training stability, and heavy DR tends to result in instability or even failure in training. To relieve this conflict, we propose a new algorithm named Domain Decomposition (DD) that decomposes the randomized domain according to environments and trains a separate RL policy for each part. This decomposition stabilizes the training of each RL policy, and as we prove theoretically, the adaptability of the overall policy can be preserved. Our simulation results verify that DD really improves stability in training while preserving ideal adaptability. Further, we complete a complex real-world vision-based patrolling task using DD, which demonstrates DD’s practicality. A video is attached as supplementary material.
EVALUATION OF THE DIGITALIZATION DEGREE OF NON-PROFIT ORGANIZATIONS IN THE REPUBLIC OF SAKHA (YAKUTIA) (ON THE EXAMPLE OF LIBRARIES AND MUSEUMS)

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Application Research on Higher Mathematics Network Teaching Platform of Higher Vocational Colleges Combined with Computer in Cloud Computing Era

Abstract With modern people’s needs become more diverse, the previous computer network technology has been far from meeting people’s needs, so the computer technology in the continuous development and upgrading. Computing technology has been applied more and more widely. How to maximize the function of computer technology and network management system in education.
Using blockchain technology in credit rating industry to promote an innovative bond-pays model

Purpose This paper aims to propose an innovative blockchain-based system enabling implementation of a bond-pays model in credit rating industry. Issuer-pays model has led to conflict of interest resulting in rating shopping and inflation. Alternative business models have their own problems, e.g. investor-pays model suffers from “free rider” and public dissemination challenges, whereas government-controlled business models can lead to market distortion. Bond-pays model has been difficult to implement owing to operational difficulties in managing co-ordination amongst multiple entities involved, often with conflicting goals. Blockchain technology enables inter-organizational systems that foster trust amongst non-trusting entities, facilitating business functions such as credit rating to be carried out. Design/methodology/approach This paper outlines current processes in credit rating business that has led to repeated rating failures and proposes a new set of processes, leveraging capabilities of blockchain technology to enable implementation of an arms-length bond-pays model. Findings A proof-of-concept system, namely, rating chain has been designed to implement a small part of the proposed model to establish technical feasibility in a blockchain environment. Practical implications A fully functional blockchain-based system on bond-pays business model, if built and adopted, could impact how credit rating market functions currently and could contribute to a reduction in rating-related challenges. Originality/value The proposal to adopt blockchain technologies in implementing a bond-pays model in credit rating industry is a novel contribution.
Fairness improvement by combination of ABR and TCP algorithms in ABR video streaming

MPEG-DASH (MPEG Dynamic Adaptive Streaming over HTTP) is an international standard for video playback. In MPEG-DASH, the user perceived performance of video playback is determined by the interaction of ABR algorithms on higher layer (application layer) and TCP algorithms on lower layer (transport layer). In this paper, we evaluate several promising combinations of ABR algorithms in the application layer and TCP algorithms in the transport layer and show that carefully selected combinations can improve fairness among users. And we identify specific features of combinations which bring improvement of fairness.
Training Microsoft News Recommenders with Pretrained Language Models in the Loop.

News recommendation calls for deep insights of news articles' underlying semantics. Therefore, pretrained language models (PLMs), like BERT and RoBERTa, may substantially contribute to the recommendation quality. However, it's extremely challenging to have news recommenders trained together with such big models: the learning of news recommenders requires intensive news encoding operations, whose cost is prohibitive if PLMs are used as the news encoder. In this paper, we propose a novel framework, SpeedyFeed, which efficiently trains PLMs-based news recommenders of superior quality. SpeedyFeed is highlighted for its light-weighted encoding pipeline, which gives rise to three major advantages. Firstly, it makes the intermedia results fully reusable for the training workflow, which removes most of the repetitive but redundant encoding operations. Secondly, it improves the data efficiency of the training workflow, where non-informative data can be eliminated from encoding. Thirdly, it further saves the cost by leveraging simplified news encoding and compact news representation. SpeedyFeed leads to more than 100$\times$ acceleration of the training process, which enables big models to be trained efficiently and effectively over massive user data. The well-trained PLMs-based model significantly outperforms the state-of-the-art news recommenders in comprehensive offline experiments. It is applied to Microsoft News to empower the training of large-scale production models, which demonstrate highly competitive online performances. SpeedyFeed is also a model-agnostic framework, thus being potentially applicable to a wide spectrum of content-based recommender systems. We've made the source code open to the public so as to facilitate research and applications in related areas.
Towards Comparative Analysis of Resumption Techniques in ETL

Data warehouses are loaded with data from sources such as operational data bases. Failure of loading process or failure of any of the process such as extraction or transformation is expensive because of the non-availability of data for analysis. With the advent of e-commerce and many real time application analysis of data in real time becomes a norm and hence any misses while the data is being loaded into data warehouse needs to be handled in an efficient and optimized way. The techniques to handle failure of process to populate the data are very much important as the actual loading process. Alternative arrangement needs to be made for in case of failure so that processes of populating the data warehouse are done in time. This paper explores the various ways through which a failed process of populating the data warehouse could be resumed. Various resumption techniques are compared and a novel block based technique is proposed to improve one of the existing resumption techniques.
Lifelong Learning of Compositional Structures

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and adequately reuse them in novel combinations for solving different yet structurally related problems. Learning such compositional structures has been a significant challenge for artificial systems, due to the combinatorial nature of the underlying search problem. To date, research into compositional learning has largely proceeded separately from work on lifelong or continual learning. We integrate these two lines of work to present a general-purpose framework for lifelong learning of compositional structures that can be used for solving a stream of related tasks. Our framework separates the learning process into two broad stages: learning how to best combine existing components in order to assimilate a novel problem, and learning how to adapt the set of existing components to accommodate the new problem. This separation explicitly handles the trade-off between the stability required to remember how to solve earlier tasks and the flexibility required to solve new tasks, as we show empirically in an extensive evaluation.
L2 Engagements on Facebook: A Survey on the Network's Usefulness for Voluntary German and Japanese Learning

&lt;p&gt;In this paper the results of an online survey (n=210) regarding the usefulness of Facebook for informal L2 learning will be presented. Participants were individual Facebook users who study either German (n=118) or Japanese (n=92). By using a combination of open and closed questions, the mostly quantitative findings of the survey indicate that for most participants Facebook is a suitable tool for improving their L2 skills, because the network enables them to establish or to maintain social contacts with native speakers and to have authentic opportunities to express themselves in the L2. As particularly effective activities, participants mentioned reading and writing L2 posts on the networks’ timeline, exchanging private messages, and commenting on friends’ posts. As far as effects on L2 learning are concerned, most participants noted an improvement in their reading and writing skills, as well as an expansion of their vocabulary and cultural understanding. Some respondents, however, raised concerns about the formal correctness of the language they encounter on the network as well as insecurity regarding communication with strangers. The information participants provided on their informal use of Facebook led to the conclusion, that learners of L2 German and learners of L2 Japanese show similar tendencies in using the network for their L2 learning. In contrast to using social networks in educational settings, their informal use allows learners much more freedom to decide when, how often, with whom, on which topic, and in which way they wish to interact in their target language on the network.&lt;/p&gt;
A strong call-by-need calculus

We present a call-by-need λ-calculus that enables strong reduction (that is, reduction inside the body of abstractions) and guarantees that arguments are only evaluated if needed and at most once. This calculus uses explicit substitutions and subsumes the existing strong-call-by-need strategy, but allows for more reduction sequences, and often shorter ones, while preserving the neededness. The calculus is shown to be normalizing in a strong sense: Whenever a λ-term $t$ admits a normal form $n$ in the λ-calculus, then any reduction sequence from $t$ in the calculus eventually reaches the normal form $n$. We also exhibit a restriction of this calculus that has the diamond property and that only performs reduction sequences of minimal length, which makes it systematically better than the existing strategy.
Keyframe Generation Method via Improved Clustering and Silhouette Coefficient for Video Summarization

In order to solve the issue that the traditional k-means algorithm falls into the local optimal solution in video summarization due to unreasonable initial parameter setting, a video summarization generation algorithm by using improved clustering and silhouette coefficient was proposed. Firstly, color features and texture features are extracted and fused from the decomposed video frames. Secondly, the hierarchical clustering algorithm is used to obtain the initial clustering results. And then, the improved k-means algorithm with silhouette coefficient is introduced to optimize the initial clustering results. Finally, the nearest frame from the cluster center is selected as the keyframe, and all the final keyframes are arranged in the order of the time sequence in the original video to constitute video summarization. The proposed algorithm is evaluated on the benchmark Open Video Database dataset with an average 71% precision, 84% recall rate, and 76% F-score, which is higher than state-of-the-art video summarization methods. Moreover, it generates video keyframes that are closer to user summaries, and it improves effectively the overall quality of the generated summary.
Cryptourbanomics: A Method to Boost Urban Sustainability with Blockchain Technology

Cryptourbanomics puts forward the idea that there are forces and capital in our society that cannot be dismissed or neglected but that the System (understood as the Establishment or Status-quo) has failed to acknowledge or been unable to address. These social forces have strong ideological, cultural, or identity components, sometimes related to an unrealized Right to the City (Lefèvre, 1968). The social capital behind those forces are often citizens who gave up—the so-called drop-outs because they lost their faith in the System and prefer living in their own world. Blockchain is the technology that empowers these unheard social forces and capital. However, blockchain will remain as an Anti-System technology until it finds a fit within the Establishment until the Status-quo acknowledges and ushers it. Cryptourbanomics is a novel method that brings into the blockchain those societal challenges that the System leaves unsolved. And because today’s societal challenges mostly take place in urban environments, the Cryptourbanomics method focuses on the overall urban sustainability and analyses them with a blockchain lens. The Cryptourbanomics method includes an array of blockchain tools to tackle legacy societal challenges yet unsolved by the System with a more decentralised, distributed, transparent and neutral approach. This paper shows how the Cryptourbanomics method can help deliver on Urban Sustainability by shifting powers, from the Establishment to Communities, and it showcases this with a use case within the Right to the City, that is the Right to Work.