Reviewerly/exHarmony huggingface dataset:
The Effects of Vocabulary Self-Collection Technique Toward Students’ Vocabulary Mastery At The Eighth Grade of SMP 1 Lasusua

This study was aimed to find out the effect of vocabulary self- collection strategy technique toward students vocabulary mastery. A further concern of this study was to investigate the significant difference of vocabulary mastery between two groups who were taught by using vocabulary self-collection strategy. The research design of this study was a quasi experimental research. The population  was  the  eighth  grade  students  of  SMP  Negeri 1 Lasusua  in  the academic year of 2018/2019. The samples consisted of 66 students. The data were obtained by giving vocabulary test to the VIII A as experimental group and VIII C as control group. The study was started by giving pre-test, treatments and post-test to both groups. Vocabulary self-collection strategy was used as treatment in the experimental group. The results of the test were analyzed by using t-test formula to know the difference of the students’ achievement in vocabulary mastery between two groups.  In the pre-test result, the mean score of experimental group was 65 and the control group was 65,16. However, the result of post-test was increased. The analysis of the test result showed that the students` improvement of experimental group was higher than control group. In the post-test, the mean score of the experimental group was 77,50 while the control group got 70,78. The result of the t-test of mean difference was 3,549 and t-table was 1,997. It means that t-value was higher than t-table (3,549 >1,997). The computation showed that the alternative hypothesis (Ha) stating that “there is a significant difference in vocabulary mastery between the students who are taught by using vocabulary self- collection strategy. While, the null hypothesis (H0) stating that there is no significant difference in vocabulary mastery between the students’ who are taught by using vocabulary self-collection. Based on the hypotheses the conclusion that vocabulary self-collection strategy is more effective to be implemented in teaching vocabulary to improve students` vocabulary mastery.
Non-exchangeable feature allocation models with sublinear growth of the feature sizes

Feature allocation models are popular models used in different applications such as unsupervised learning or network modeling. In particular, the Indian buffet process is a flexible and simple one-parameter feature allocation model where the number of features grows unboundedly with the number of objects. The Indian buffet process, like most feature allocation models, satisfies a symmetry property of exchangeability: the distribution is invariant under permutation of the objects. While this property is desirable in some cases, it has some strong implications. Importantly, the number of objects sharing a particular feature grows linearly with the number of objects. In this article, we describe a class of non-exchangeable feature allocation models where the number of objects sharing a given feature grows sublinearly, where the rate can be controlled by a tuning parameter. We derive the asymptotic properties of the model, and show that such model provides a better fit and better predictive performances on various datasets.
Meningkatkan Keterampilan Menulis Teks Ulasan Melalui Metode Membaca SQ3R pada Buku FiksiPembelajaran Menulis Teks Ulasan Dengan SQ3R

Artikel ini akan mendeskripsikan pembelajaran menulis teks ulasan dengan menggunakan SQ3R dan memaparkan bagaimana kemampuan menulis teks ulasan peserta didik kelas VIII SMPN 1 Warungkiara sebelum dan setelah menggunakan SQ3R . Metode yang digunakan dalam penelitian ini adalah kuasi eksperimen. Sampel penelitian kelas kontrol berjumlah 36 orang dan kelas eksperimen berjumlah 36 orang. Teknik pengumpulan data melalui tes, observasi, dan angket. Setelah dilakukan penelitian diketahui bahwa metode membaca SQ3R cukup efektif meningkatkan kemampuan peserta didik dalam menulis teks ulasan dari nilai rata-rata 42,75 menjadi 75,81. Keefektifan model membaca SQ3R dibuktikan dengan uji hipotesis terhadap data hasil taraf signifikansi 0,05 dan diperoleh hasil bahwa Berdasarkan data diketahui nilai sig. (2-tailed) adalah sebesar 000 &lt; 0,05, maka H 0 ditolak dan H a diterima. Sehingga dapat disimpulkan bahwa ada peningkatan kemampuan menulis teks ulasan peserta didik dengan metode membaca SQ3R. Dengan menggunakan metode membaca SQ3R, dapat meningkatkan peserta didik menulis teks ulasan. Apakah metode membaca SQ3R dapat dilakukan pada teks lainnya? Hal ini dapat menjadikan bahan pertimbangan untuk peneliti lain. Kata kunci : menulis, teks ulasan, membaca, SQ3R . This article will describe learning to write review texts using SQ3R and explain how the ability to write text reviews for grade VIII students at SMP Negeri 1 Warungkiara before and after using SQ3R. The method used in this research is quasi-experimental. The control class research sample was 36 people and the experimental class was 36 people. Data collection techniques through tests, observations, and questionnaires. After conducting research it is known that the SQ3R reading method is quite effective in increasing the ability of students in writing review texts from an average value of 42.75 to 75.81. The effectiveness of the SQ3R reading model is proven by the hypothesis test of the significance level of 0.05 results and the results show that based on the data the sig value is known. (2-tailed) is 000 &lt;0.05, then H0 is rejected and Ha is accepted. So it can be concluded that there is an increase in the ability to write text reviews of students with the SQ3R reading method. By using the SQ3R reading method, students can improve writing review texts. Can the SQ3R method be read in other texts? This can be taken into consideration for other researchers. Keywords : writing, review text, SQ3R, reading.
Toward Operational Safety Verification of AI-Enabled CPS (Student Abstract)

AI-enabled Cyber-physical systems (CPS) such as artificial pancreas (AP) or autonomous cars are using machine learning to make several critical decisions. The system is subject to inputs and scenarios which are not observed during training and the expected outputs are not known. Hence, popular model based verification techniques that characterize behavior of a control system before deployment using predictive models may be inaccurate and often result in incorrect safety analysis results. In addition, regulatory agencies are required to regulate safety-critical AI enabled CPS to ensure their operational safety. However, high complexity of the system result in myriad of safety concerns all of which may not only be comprehensively tested before deployment but also may not even be detected during design and testing phase. In this work, we propose a tool to help regulatory agencies compare the operation of the CPS with the specifications given by the manufacturer to ensure that the operation results conform with the safety assured design of a CPS.
Sparse Regression via Range Counting.

The sparse regression problem, also known as best subset selection problem, can be cast as follows: Given a set S of n points in ℝ^d, a point y∈ ℝ^d, and an integer 2 ≤ k ≤ d, find an affine combination of at most k points of S that is nearest to y. We describe a O(n^{k-1} log^{d-k+2} n)-time randomized (1+e)-approximation algorithm for this problem with d and e constant. This is the first algorithm for this problem running in time o(n^k). Its running time is similar to the query time of a data structure recently proposed by Har-Peled, Indyk, and Mahabadi (ICALP'18), while not requiring any preprocessing. Up to polylogarithmic factors, it matches a conditional lower bound relying on a conjecture about affine degeneracy testing. In the special case where k = d = O(1), we provide a simple O_δ(n^{d-1+δ})-time deterministic exact algorithm, for any δ > 0. Finally, we show how to adapt the approximation algorithm for the sparse linear regression and sparse convex regression problems with the same running time, up to polylogarithmic factors.
Not all Mistakes are Equal

In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks (DNNs) have become the dominant approach to develop classifiers due to their excellent performance. When training and evaluating the performance of DNNs, it is normally assumed that the cost of all misclassification errors are equal. However, this is unlikely to be true in practice. Incorrect classification predictions can cause an agent to take inappropriate actions. The costs of these actions can be asymmetric, vary from agent-to-agent, and depend on context.In this paper, we discuss the importance of considering risk and uncertainty quantification together to reduce agents' cost of making misclassifications using deep classifiers.
Скринкасты в дистанционном обучении пользователей библиотек

The article describes the necessity of developing the distance learning of the library users using the modern web-technologies. The possibility of using screencasting in a library, experience of the National Library of Belarus in creation of video tutorials, series of steps in screencast development are described
LibGuides: Finding and Using Free-Use Online Content: Copyright and Licensing

Looking for free images, sound, music or video to use in your PowerPoint, e-Learning video or other digital projects? Curated here is a list of databases containing free content. Check the license terms before use.
To some questions about the library I.D. Popco

Studying personal libraries allows you to not only get to know the bibliophile better – to understand what he was interested in, how educated he was, what he was like in terms of intellectual development, to know his political views, in short, to understand what he was like as a person personality. In addition, with the help of this kind of research, in addition, you can get a certain idea of the book culture of the collector’s lifetime, learn more about his circle of friends and much more. From this point of view, Ivan Diomidovich Popko, a famous military and public figure in the North Caucasus, is of great interest.
The Fox and the Hound: Comparing Fully Abstract and Robust Compilation

We prove a theorem relating fully abstract compilation (FAC) to robust compilation (preservation of satisfaction of arbitrary hyperproperties under adversarial contexts), showing that the former implies some variant of the latter, thus making the security guarantees of FAC more explicit. We illustrate our results with a simple example.
Systems-Thinking Heuristics for the Reconciliation of Methodologies for Design and Analysis for Information Systems Engineering

Many competing, complementary, generic, or specific methodologies for design and analysis co-exist in the field of Information System Engineering. The idea of reconciling these methodologies and their underlying theories has crossed the minds of researchers many times. In this paper, we inquire into the nature of such reconciliation using the interpretivist research paradigm. This paradigm acknowledges the existence of diverse points of view as ways of seeing and experiencing the world through different contexts. We examine why it might be impossible to reconcile these methodologies that each represents a point of view. Instead of searching for the one (overarching, universal, global, ultimate) methodology that reconciles all others, we explain why we should think about reconciliation as an ongoing practice. We propose to the community a set of heuristics for this practice. The heuristics are a result of our experience in reconciling a number of methods that we created as part of our research during the past 20 years. We illustrate the use of the heuristics with an example of use cases and user stories. We believe these heuristics to be of interest to the Information Systems Engineering community.
EXPERIENCE OF INTERACTION BETWEEN A TECHNICAL UNIVERSITY AND A CHILDREN’S TECHNOPARK ON THE EXAMPLE OF THE GENERAL DEVELOPMENT PROGRAM “AUTOQUANTUM”

The article describes an experience of cooperation between higher professional and additional general education institutions on the example of interaction between a technical university and a children’s technopark, participation of teachers, postgraduate an graduate students from Volgograd State Technical University in implementation of the general development education program “Autoquantum” of the structural division “Quantorium”.
Source Attribution: Recovering the Press Releases Behind Health Science News

We explore the task of intrinsic source attribution: inferring which portions of a derived document were adapted from an unobserved source document. Specifically, we model the relationship between news articles and their press release sources using a dataset of 64,784 health science news articles and 23,068 press releases. We approach the problem at the sentence level and work with science journalism professors to develop a four point Likert scale describing the extent to which a news article sentence is derived from the content in the corresponding press release. Because manual annotation of news article - press release pairs is time-consuming, we turn to a mix of expert, non-expert, and heuristic-based annotation to label our dataset. After a small pilot study, which found that humans, when only able to view the text of the news article, struggle to identify which content is derived or not, we compare four different sentence regression models on the task. We find that modeling a sentence's context in the entire document is important, with the best performing model, a sequence regression model with BERT token representations, achieving a spearman's ρ of 0.49 and NDCG@1 of 0.60 on the expert-labeled test set. Examining the model's predictions, we find that it successfully identifies copied or closely paraphrased sentences in articles with a mix of derived and original content, but struggles to differentiate between loosely paraphrased and original sentences in articles with mostly original writing.
Emergent Routing Strategies in the Lightning Network

In payment channel networks, such as the Bitcoin native Lightning Network, the routing nodes receive a fee as compensation for displaced liquidity, time value of money and operational costs. Curren ...
The impacts of optimization algorithm and basis size on the accuracy and efficiency of variational quantum eigensolver based on unitary coupled cluster ansatz

Variational quantum eigensolver (VQE) is demonstrated to be the promising methodology for quantum chemistry based on near-term quantum devices. However, many problems are yet to be investigated for this methodology, such as the influences of optimization algorithm and basis size on the accuracy and efficiency for quantum computing. To address these issues, five molecules (H2, LiH, HF, N2 and F2) are studied in this work based on the VQE method using unitary coupled cluster (UCC) ansatz. The performance of the gradient optimization L-BFGS-B is compared with that of the direct search method COBYLA. The former converges more quickly, but the accuracy of energy surface is a little lower. The basis set shows a vital influence on the accuracy and efficiency. A large basis set generally provides an accurate energy surface, but induces a significant increase in computing time. The 631g basis is generally required from the energy surface of the simplest H2 molecule. For practical applications of VQE, complete active space (CAS) is suggested based on limited quantum resources. With the same number of qubits, more occupied orbitals included in CAS gives a better accuracy for the energy surface and a smaller evaluation number in the VQE optimization. Additionally, the electronic structure, such as filling fraction of orbitals, the bond strength of a molecule and the maximum nuclear charge also influences the performance of optimization, where half occupation of orbitals generally requires a large computation cost.
EXPLORING THE LIMITS OF BEAT TEMPO WITH AN ILLUSION OF INFINITE TEMPO CHANGE IN A METRICAL PATTERN

Humans have the ability to synchronise with sounds divided by equal intervals and predict when the next sound is coming, as opposed to reacting to them. By creating a pulse within ourselves based o ...
Quasirandom Quantum Channels.

Mixing (or quasirandom) properties of the natural transition matrix associated to a graph can be quantified by its distance to the complete graph. Different mixing properties correspond to different norms to measure this distance. For dense graphs, two such properties known as spectral expansion and uniformity were shown to be equivalent in seminal 1989 work of Chung, Graham and Wilson. Recently, Conlon and Zhao extended this equivalence to the case of sparse vertex transitive graphs using the famous Grothendieck inequality. Here we generalize these results to the non-commutative, or 'quantum', case, where a transition matrix becomes a quantum channel. In particular, we show that for irreducibly covariant quantum channels, expansion is equivalent to a natural analog of uniformity for graphs, generalizing the result of Conlon and Zhao. Moreover, we show that in these results, the non-commutative and commutative (resp.) Grothendieck inequalities yield the best-possible constants.
Designing art galleries (Media Exposition)

We present a method for generating interesting levels based on several NP-hardness reductions for a puzzle game based on the Art Gallery problem.
PoET-BiN: Power Efficient Tiny Binary Neurons

RESUME Le succes des reseaux de neurones dans la classification des images a inspire diverses implementations materielles sur des systemes embarques telles que des FPGAs, des processeurs embarques et des unites de traitement graphiques. Ces systemes sont souvent limites en termes de puissance. Toutefois, les reseaux de neurones consomment enormement a travers les operations de multiplication/accumulation et des acces memoire pour la recuperation des poids. La quantification et l’elagage ont ete proposes pour resoudre ce probleme. Bien que efficaces, ces techniques ne prennent pas en compte l’architecture sous-jacente du materiel utilise. Dans ce travail, nous proposons une implementation econome en energie, basee sur une table de verite, d’un neurone binaire sur des systemes embarques a ressources limitees. Une approche d’arbre de decision modifiee constitue le fondement de la mise en œuvre proposee dans le domaine binaire. Un acces de LUT consomme beaucoup moins d’energie que l’operation equivalente de multiplication/accumulation qu’il remplace. De plus, l’algorithme modifie de l’arbre de decision elimine le besoin d’acceder a la memoire. Nous avons utilise les neurones binaires proposes pour mettre en œuvre la couche de classification de reseaux utilises pour la resolution des jeux de donnees MNIST, SVHN et CIFAR-10, avec des resultats presque a la pointe de la technologie. La reduction de puissance pour la couche de classification atteint trois ordres de grandeur pour l’ensemble de donnees MNIST et cinq ordres de grandeur pour les ensembles de donnees SVHN et CIFAR-10.----------ABSTRACT The success of neural networks in image classification has inspired various hardware implementations on embedded platforms such as Field Programmable Gate Arrays, embedded processors and Graphical Processing Units. These embedded platforms are constrained in terms of power, which is mainly consumed by the Multiply Accumulate operations and the memory accesses for weight fetching. Quantization and pruning have been proposed to ad-dress this issue. Though effective, these techniques do not take into account the underlying architecture of the embedded hardware. In this work, we propose PoET-BiN, a Look-Up Table based power efficient implementation on resource constrained embedded devices. A modified Decision Tree approach forms the backbone of the proposed implementation in the binary domain. A LUT access consumes far less power than the equivalent Multiply Accumulate operation it replaces, and the modified Decision Tree algorithm eliminates the need for memory accesses. We applied the PoET-BiN architecture to implement the classification layers of networks trained on MNIST, SVHN and CIFAR-10 datasets, with near state-of-the art results. The energy reduction for the classifier portion reaches up to six orders of magnitude compared to a floating point implementations and up to three orders of magnitude when compared to recent binary quantized neural networks.
The Difference Lambda-Calculus: A Language for Difference Categories.

Cartesian difference categories are a recent generalisation of Cartesian differential categories which introduce a notion of arrows satisfying an analogue of the Kock-Lawvere axiom, with the axioms of a Cartesian differential category being satisfied only up to an infinitesimal perturbation. In this work, we construct a simply-typed calculus in the spirit of the differential lambda-calculus equipped with syntactic infinitesimals and show how its models correspond to difference lambda-categories, a family of Cartesian difference categories equipped with suitably well-behaved exponentials.
Improving Energy-Efficiency through Smart Data Placement in Hadoop Clusters

Hadoop, a pioneering open source framework, has revolutionized the big data world because of its ability to process vast amounts of unstructured and semi-structured data. This ability makes Hadoop the ‘go-to’ technology for many industries that generate big data, thus it also aids in being cost effective, unlike other legacy systems. Hadoop MapReduce is used in large scale data parallel applications to process massive amounts of data across a cluster and is used for scheduling, processing, and executing jobs. Basically, MapReduce is the right hand of Hadoop, as its library is needed to process these large data sets. In this research thesis, this study proposes a smart framework model that profiles MapReduce tasks with the use of Machine Learning (ML) algorithms to effectively place the data in Hadoop clusters; activate only sufficient number of nodes to accomplish the data processing within the planned deadline time for the task. The model will ensure achieving energy efficiency by utilizing the minimum number of necessary nodes, with maximum utilization and least energy consumption to reduce the overall cost of operations in data centers that deploy the Hadoop clusters.
Girls' Multimedia Learning Experiences on Skills Development in Rukwa and Dodoma Regions, Tanzania

This article is a case study of the Girls Inspire Project (GIP) of the Institute of Adult Education in in Rukwa and Dodoma regions in Tanzania. It aimed at exploring the GIP girls' multimedia learning experiences in skills development. Specifically, it focused at exploring on how the girls participate in skills development through multimedia learning as well as their multimedia learning experiences. The study employed exploratory research design with qualitative research approach. It involved 32 girls by snowball sampling. The researcher analyzed data using content analysis. Findings indicate that; the girls participated in multimedia learning using LCD projectors, conducted discussions and later, practical sessions. Subsequently, they practiced hands-on skills in groups, with reference to learning contents and facilitators' guidance. The study concludes that the girls had a commendable multimedia learning experience on skills development. However, based on the offline learning challenges observed, the researcher recommends a feasibility study for online multimedia learning in rural parts of Tanzania prior to establishing similar interventions so as to enhance flexible access to learning contents and effective learning experiences.