Reviewerly/exHarmony huggingface dataset:
Logistic Map and Exponential Scaling Factor-Based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.
Notes on the First Digital Libraries

Digital libraries in the XXI century have become the most important sources of information in all branches of science and technology, the main direction of development of world librarianship, any information activities. The first electronic library was Project Gutenberg, founded by Michael Hart exactly 50 years ago. It became world famous only in the 90s, after the appearance of Internet and the first browsers. Hart dreamed of 10,000 digitized books, and by his admission, he achieved all the goals that he set for himself. In Russia, one of the first and most significant is Maxim Moshkov’s library (1994). This paper traces the stages of creation of these libraries and the biographies of their creators; the difficulties in their work are analyzed. Special attention is paid to the issues of copyright duration legislation.
The strategic management and its relationship with organizational success: Empirical study on the pharmaceutical industry sector in Jordan: التجديد الاستراتيجي وعلاقته بالنجاح التنظيمي: دراسة ميدانية على قطاع الصناعة الدوائية في الأردن

The study aims to test and evaluate the relationship between strategic renewal and organizational success of the pharmaceutical industry sector companies in Jordan. The descriptive and analytical approach was used. and the study population consists of (1000) leaders and managers in the top and middle management level in the pharmaceutical industries sector companies in Jordan. And a random sample was chosen of (420) participants. In order to reach the objectives of the study, a questionnaire was developed to collect data. The study concluded that the independent variable, which is strategic renewal, is available in the companies at a high level. Where the exploration of opportunities came in the first rank, which corresponds to a great degree of appreciation with a mean (4.03).Then came the coordinated strategic action, strategic imagination, the exploitation of opportunities, and the genesis of new ideas, ranked respectively in terms of relative importance. It was found that the researched companies achieved a high level of organizational success with an overall average of (3.81), in addition to the existence of a statistically significant correlation between strategic renewal and organizational success, and the study recommended maintaining the level of practical application of strategic renewal in the pharmaceutical industry sector companies in Jordan. And that is through practical steps that companies follow and work to develop and sustain the level of organizational success by developing and strengthening the process of strategic renewal in all functional areas of companies.
Automatic Classification of Long Mongolian Text Based on Word Embedding Distributed Representation Deep Learning Model

With the development of information technology of Mongolian language and the publication of international standard of Mongolian language coding,Mongolian electronic texts have sprung up in recent years. The research of automatic classification technology in the field of Mongolian information processing has received much concern compared to the time-consuming effort of manual processing and classification. The current research on long Mongolian text automatic classification mainly relies on traditional algorithms such as Naive Bayes and Support Vector Machine, as well as sparse feature representation methods based on word frequency like TF-IDF or One-Hot. However, in terms of the characteristics of Mongolian language itself, Mongolian sentences in news texts are usually long, and there are some problems such as difficulty in extracting semantic features and poor classification effect by using traditional algorithms. In this paper, the combination model of CNN and RNN (CNN+RNN Model) which is characterized by word embedding distributed expression is proposed to carry out the experiment of automatic classification of long Mongolian text which takes news text as data. Experimental results show that compared with CNN model or RNN model used alone, the combination model is superior to the model used alone in various indexes such as precision rate, loss rate, accuracy rate, recall rate and F1 value, and can effectively process longer Mongolian text.
Procesos de enseñanza aprendizaje de la alfabetización con perspectiva sociocultural considerando las nuevas culturas escritas

The article presents partial results of the general approach of the doctoral thesis, focused on knowing and understanding the literacy processes considering the local literacy practices from children and their mothers do in everyday life. It was done in a neighborhood of San Luis Potosi (Mexico). In applied research with a sociocultural perspective and from a qualitative paradigm, I used data collection techniques typical of ethnography through an interactionist analysis. The main contributions of the proposal of pedagogical innovation built were: the design considering the baggage of the new literacy practices from the context and that the mothers possess, and the development of symmetrical interpersonal relationships through empathetic dialoguein the recognition of dignity and respect towards children and their mothers. Therefore, a higher level of consciousness was achieved in the learning process and the will as superior psychological functions
Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.
Systematic Generalization with Edge Transformers

Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks. To tackle this challenge, we propose Edge Transformer, a new model that combines inspiration from Transformers and rule-based symbolic AI. The first key idea in Edge Transformers is to associate vector states with every edge, that is, with every pair of input nodes -- as opposed to just every node, as it is done in the Transformer model. The second major innovation is a triangular attention mechanism that updates edge representations in a way that is inspired by unification from logic programming. We evaluate Edge Transformer on compositional generalization benchmarks in relational reasoning, semantic parsing, and dependency parsing. In all three settings, the Edge Transformer outperforms Relation-aware, Universal and classical Transformer baselines.
Quantifying Contextual Aspects of Inter-annotator Agreement in Intertextuality Research

We report on an inter-annotator agreement experiment involving instances of text reuse focusing on the well-known case of biblical intertextuality in medieval literature.We target the application use case of literary scholars whose aim is to document instances of biblical references in the 'apparatus fontium' of a prospective digital edition.We develop a Bayesian implementation of Cohen's  for multiple annotators that allows us to assess the influence of various contextual effects on the inter-annotator agreement, producing both more robust estimates of the agreement indices as well as insights into the annotation process that leads to the estimated indices.As a result, we are able to produce a novel and nuanced estimation of inter-annotator agreement in the context of intertextuality, exploring the challenges that arise from manually annotating a dataset of biblical references in the writings of Bernard of Clairvaux.Among others, our method is able to unveil the fact that the obtained agreement depends heavily on the biblical source book of the proposed reference, as well as the underlying algorithm used to retrieve the candidate match.Finally, a discussion of the hurdles encountered by annotators supplements the results of the statistical analysis, contributing a qualitative insight into the difficulties involved in the identification of literary text reuse.
Scheduling - A Secret Sauce For Resource Disaggregation

This technical report describes the design & implementation of a constraint-based framework for scheduling & resource allocation in a disaggregated data center (DDC) where we build logical ...
oflibnumpy &amp; oflibpytorch: Optical Flow Handling and Manipulation in Python

We present oflibnumpy and oflibpytorch, an optical flow library based on NumPy arrays and PyTorch tensors, respectively. It provides a structured approach to the representation of optical flow, i.e <em>2</em>D vector fields, as a custom class offering a number of methods to apply, manipulate, analyse, and visualise the flow. The library takes into account the two possible frames of reference in optical flow calculation, namely the source (first frame) and target (second frame). The collection of methods and their rigorous mathematical underpinning makes the library broadly applicable to any project that uses flow fields. It is implemented as a Python 3 package whose source can be found on GitHub, and which can be installed either from the git repository or the Python Package Index (PyPI).
The Current State of Finnish NLP

There are a lot of tools and resources available for processing Finnish. In this paper, we survey recent papers focusing on Finnish NLP related to many different subcategories of NLP such as parsing, generation, semantics and speech. NLP research is conducted in many different research groups in Finland, and it is frequently the case that NLP tools and models resulting from academic research are made available for others to use on platforms such as Github.
К ВОПРОСУ О СМЕШАННОМ ОБУЧЕНИИ В ПРЕПОДАВАНИИ ИНОСТРАННЫХ ЯЗЫКОВ

Blended learning being one of the relevant educational approach at the current moment directly connected with the conditions of coronavirus pandemic lockdown combines teacher’s face-to-face and online learning. It involves elements of independent student’s control of the educational schedule, place and pace of learning and integration of teacher’s experience. It allows developing students’ planning skills, self-control and self-regulation giving them much freedom. In this case teacher-assisted learning concerning students’ needs seems to be an important part. Because students are eager however to have teacher’s presence in the distant classroom as he demonstrates patterns of behavior and thinking, ways of building relationships. Blended learning has many advantages combining distance and traditional teaching methods: it provides the opportunity for students and teacher to interact with each other at on-line platforms. It also creates a favourable educational environment, the mood to achieve a result, motivation to study. Participants instantly receive feedback, discuss material, ask questions. Collaborative learning and student interaction enhance cognitive abilities, develop their emotional intelligence. The blended model provides more freedom for students: choose of material, pace, time and place of study. The teacher has more freedom as well: in the presentation of training materials, monitoring and evaluation due to reducing his time to test as it is performed online.
Hubungan Disiplin Belajar dengan Prestasi Belajar Siswa Kelas V SD Negeri 25 Mattirowalie Kecamatan Tanete Riattang Barat Kabupaten Bone

: This study is a correlational study that aims to determine whether or not there is a relationship between learning discipline and learning achievement of fifth grade students at SD Negeri 25 Mattirowalie, Tanete Riattang Barat District, Bone Regency. The population in this study were all fifth grade students of SD Negeri 25 Mattirowalie, Tanete Riattang Barat District, Bone Regency. while the number of samples in this study were 47 students. Data collection is done by providing questionnaires and documentation. The data analysis technique used in this research is descriptive statistical analysis and inferential statistical analysis. Based on the results of descriptive analysis obtained 102 learning disciplines and a percentage of 84.62% in the very good category and student achievement with an average of 84.40 and a percentage of 84.80% in the very good category. The results of inferential statistical analysis obtained tcount (7.9180) ttable (1.67943) at a significant level of 5%. Thus H0 is rejected and H1 is accepted. It can be concluded that there is a significant relationship between learning discipline and learning achievement of fifth grade students at SD Negeri 25 Mattirowalie, Tanete Riattang Barat sub-district, Bone Regency.
PengeloaanBerkas Elektronik Untuk Meningkatkan Kinerja Guru

Madrasah Aliyah School DDI Galesong Baru is a school that has a number of teachers who are very experienced in teaching. However, there are only a few teachers who are able to master computer well, almost all teachers in the partner environment are still unfamiliar in using computer equipment, especially in making files, for example, making material files for teaching materials in class or making exam questions in the form of file versions. electronics quickly.  OCR (Optical Character Recognition) is an effective solution for the process of converting printed documents to digital documents. Transferring print files into electronic files can be done by scanning, conversion, importing. Whereas electronic archive storage can be done online, offline. The method used in this study is a survey, discussion and presentation approach. The results obtained from the training at the Madrasah Aliyah DDI Galesong Baru school were quite positive, the partners expressed their satisfaction with the training that had been carried out.
ВПЛИВ МІЖОСОБИСТІСНИХ ВІДНОСИН ЯК СКЛАДНИКА КОРПОРАТИВНОЇ КУЛЬТУРИ НА ОРГАНІЗАЦІЙНУ ЕФЕКТИВНІСТЬ

Panasenko Yu. A., Tashchenko A. Yu. The influenceof interpersonal relations as a component of corporateculture on organizational efficiency. – Article.The article is devoted to the substantiation ofthe causal connection of interpersonal relations as acomponent of corporate culture and organizationalefficiency. It is done to better understand the need forcertain measures of corporate social responsibility. Theauthors interpret the organizational efficiency in linewith the system approach in terms of achieving socialgoals and missions through measures of corporate socialresponsibility on the example of its internal component.Internal corporate social responsibility is aimed not onlyat compliance with legal norms, but also at practicing thevoluntary response of the organization to the problemsof its employees. Corporate culture is introduced into thefield of research attention as an important determinantof the development of a strong internal organizationalstructure. The works of the classics of sociology givegrounds to confirm its connection with organizationalefficiency, in particular, in terms of the theory ofbureaucracy (M. Weber) and the theory of social space(P. Bourdieu). Corporate culture means particularqualities of values, rules of conduct and communicationof employees of the organization, the typologizationof which many scientists were engaged in. The authorsmentioned the typologization approaches of W. Ouchi,F. Trompenaars and Ch. Hampden-Turner, G. Hofstede,Ch. Handy, M. Tulenkov and O. Kovtun. Also, theauthors consider interpersonal relations on the basis ofcorporate culture types identified by F. Trompenaars andCh. Hampden-Turner: universalistic – particularistic,individualistic – collectivistic, descriptive – ascriptive.As a prerequisite for verifying the presence or absenceof connection between interpersonal relationships andinternal corporate social responsibility empirically,it has been proposed to summarize the most relevantindicators of interpersonal relationships as part ofcorporate culture and to get acquainted with thehypotheses about the employee needs in this context.
A finite mixture mixed proportion regression model for classification problems in longitudinal voting data

Continuous clustered proportion data often arise in various areas of the social and political sciences where the response variable of interest is a proportion (or percentage). An example is the behavior of the proportion of voters favorable to a political party in municipalities (or cities) of a country over time. This behavior can be different depending on the region of the country, giving rise to groups (or clusters) with similar profiles. For this kind of data, we propose a finite mixture of a random effects regression model based on the L-Logistic distribution. A Markov chain Monte Carlo algorithm is tailored to obtain posterior distributions of the unknown quantities of interest through a Bayesian approach. To illustrate the proposed method, with emphasis on analysis of clusters, we analyze the proportion of votes for a political party in presidential elections in different municipalities observed over time, and then identify groups according to electoral behavior at different levels of favorable votes.
Benchmarking Meta-embeddings: What Works and What Does Not.

In the last few years, several methods have been proposed to build meta-embeddings. The general aim was to obtain new representations integrating complementary knowledge from different source pre-trained embeddings thereby improving their overall quality. However, previous meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. In this paper we propose a unified common framework, including both intrinsic and extrinsic tasks, for a fair and objective meta-embeddings evaluation. Furthermore, we present a new method to generate meta-embeddings, outperforming previous work on a large number of intrinsic evaluation benchmarks. Our evaluation framework also allows us to conclude that previous extrinsic evaluations of meta-embeddings have been overestimated.
Guilt by Association: Emotion Intensities in Lexical Representations.

What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE

Recent studies show that advanced priors play a major role in deep generative models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has achieved impressive results. However, due to the nature of model design, an exemplar-based model usually requires vast amounts of data to participate in training, which leads to huge computational complexity. To address this issue, we propose Bayesian Pseudocoresets Exemplar VAE (ByPE-VAE), a new variant of VAE with a prior based on Bayesian pseudocoreset. The proposed prior is conditioned on a small-scale pseudocoreset rather than the whole dataset for reducing the computational cost and avoiding overfitting. Simultaneously, we obtain the optimal pseudocoreset via a stochastic optimization algorithm during VAE training aiming to minimize the Kullback-Leibler divergence between the prior based on the pseudocoreset and that based on the whole dataset. Experimental results show that ByPE-VAE can achieve competitive improvements over the state-of-the-art VAEs in the tasks of density estimation, representation learning, and generative data augmentation. Particularly, on a basic VAE architecture, ByPE-VAE is up to 3 times faster than Exemplar VAE while almost holding the performance. Code is available at \url{https://github.com/Aiqz/ByPE-VAE}.
QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning.

Web servers scaled across distributed systems necessitate complex runtime controls for providing quality of service (QoS) guarantees as well as minimizing the energy costs under dynamic workloads. This paper presents a QoS-aware runtime controller using horizontal scaling (node allocation) and vertical scaling (resource allocation within nodes) methods synergistically to provide adaptation to workloads while minimizing the power consumption under QoS constraint (i.e., response time). A horizontal scaling determines the number of active nodes based on workload demands and the required QoS according to a set of rules. Then, it is coupled with vertical scaling using transfer Q-learning, which further tunes power/performance based on workload profile using dynamic voltage/frequency scaling (DVFS). It transfers Q-values within minimally explored states reducing exploration requirements. In addition, the approach exploits a scalable architecture of the many-core server allowing to reuse available knowledge from fully or partially explored nodes. When combined, these methods allow to reduce the exploration time and QoS violations when compared to model-free Q-learning. The technique balances design-time and runtime costs to maximize the portability and operational optimality demonstrated through persistent power reductions with minimal QoS violations under different workload scenarios on heterogeneous multi-processing nodes of a server cluster.