Reviewerly/exHarmony huggingface dataset:
Teacher's Readiness For Marketing Activities As A Condition Of Quality Education

The article discusses the pedagogical aspects of improving the quality of modern education. As one of the ways to solve this problem, the authors suggest considering the marketing activities of teachers and the administration of educational organizations. The article discusses the essence of the modern interpretation of the notion “quality of education”, as well as the role and possibilities of marketing in improving the quality of education in an educational organization. The article discusses the question of how a modern teacher (and as a result, a modern educational organization) can effectively build a system of high quality education and achieve the high satisfaction of educational process subjects with the quality of education. The authors of the article present the results of an online survey of teachers of educational organizations. This survey contained questions regarding the content of professional education of teachers, their age, work experience. The research questions were also aimed at identifying the readiness of teachers for marketing activities. The survey revealed the role position of teachers as organizers of the educational process and their willingness to accept the marketing approach as a value and methodological basis of their professional activities. The degree of readiness of teachers for self-development in the field of educational marketing was also revealed. The survey results showed the most relevant content and the most effective technologies that will be applicable in the organization of additional education of teachers in the field of educational marketing.
Effective Behavioural Dynamic Coupling through Echo State Networks

This work presents a novel approach and paradigm for the coupling of human and robot dynamics with respect to control. We present an adaptive system based on Reservoir Computing and Recurrent Neural Networks able to couple control signals and robotic behaviours. A supervised method is utilised for the training of the network together with an unsupervised method for the adaptation of the reservoir. The proposed method is tested and analysed using a public dataset, a set of dynamic gestures and a group of users under a scenario of robot navigation. First, the architecture is benchmarked and placed among the state of the art. Second, based on our dataset we provide an analysis for key properties of the architecture. We test and provide analysis on the variability of the lengths of the trained patterns, propagation of geometrical properties of the input signal, handling of transitions by the architecture and recognition of partial input signals. Based on the user testing scenarios, we test how the architecture responds to real scenarios and users. In conclusion, the synergistic approach that we follow shows a way forward towards human in-the-loop systems and the evidence provided establish its competitiveness with available methods, while the key properties analysed the merits of the approach to the commonly used ones. Finally, reflective remarks on the applicability and usage in other fields are discussed.
Improvement Multi-Stage Model for Human Pose Estimation.

Multi-stage methods are widely used in detection task, and become morecompetitive than single-stage. This paper studed the improvement both in singleand multi stage model. Training methods is also metioned in this paper, likemulti {\sigma} of kernel sizes for different stages, and training steps toimprove the stability of convergance. The resulting multi-stage networkoutperforms all previous works and obtains the best performance on singleperson task of MPII.
Opinion Mining: An Approach to Feature Engineering

Sentiment Analysis or opinion mining refers to a process of identifying and categorizing the subjective information in source materials using natural language processing (NLP), text analytics and statistical linguistics.The main purpose of opinion mining is to determine the writer's attitude towards a particular topic under discussion.This is done by identifying a polarity of a particular text paragraph using different feature sets.Feature engineering in pre-processing phase plays a vital role in improving the performance of a classifier.In this paper we empirically evaluated various features weighting mechanisms against the well-established classification techniques for opinion mining, i.e.Naive Bayes-Multinomial for binary polarity cases and SVM-LIN for multiclass cases.In order to evaluates these classification techniques we use Rotten Tomatoes publically available movie reviews dataset for training the classifiers as this is widely used dataset by research community for the same purpose.The empirical experiment concludes that the feature set containing noun, verb, adverb and adjective lemmas with feature-frequency (FF) function perform better among all other feature settings with 84% and 85% correctly classified test instances for Naïve Bayes and SVM, respectively.
ANALISIS KEBUTUHAN AIR IRIGASI MENGGUNAKAN METODE CROPWAT VERSION 8.0

The Irrigation Area of Air Nipis is located in Regency of South Bengkulu at Bengkulu Province with irrigation area 3.116 Ha. Planning and management of irrigation systems is one of the important steps to determine the irrigation water requirement as a whole. The purpose of this research is aim to analyze the water requirement to get value prediction of minimum and maximum irrigation water requirement in irrigation area of Air Nipis using the CROPWAT Version 8.0 method. Irrigation water requirements obtained from CROPWAT Version 8.0 are based on climate data, soil data and plants.The parameters that were reference plant evapotranspiration, effective rainfall, soil treatment, soil data, and plants. The results of the research showed that the maximum irrigation requirement for calculation using CROPWAT 8.0 software occurred in the first 10 days of December (14,49 m3/sec), while the minimum irrigation water requirements for CROPWAT 8.0 occurs in mid to end March (0,04 m3/sec).
PEDAGOGICAL PRACTICE – POSITION, CONTRIBUTION AND IMPORTANCE IN THE PROCESS OF PREPARING OF FUTURE TEACHERS

The article deals with the analysis of basic connections of the pedagogical practice as an inevitable part of preparing of future teachers.The evidence is oriented not only to the content side of pedagogical practice and its stages, but to general activities in the area of the organization, personal work and its preparing.In realization of pedagogical practice the students obtain an abundance of impulses not only to the motivation for study, but to persistence or non-persistence in this perspective profession which is as one from the most important professions in the cultural and educational development of every country.
A Super Point Detection Algorithm Under Sliding Time Windows Based on Rough and Linear Estimators

Detecting super points from high-speed networks can effectively help to monitor networks, which is a hot topic in network fields. Most existing algorithms are carried out under discrete time windows and the results are in a certain percentage of omission. In this paper, the phenomenon of missed super points detection in discrete time windows is analyzed based on real-world traffic. Then a new algorithm, which detects the super points under sliding time windows, is proposed. Our algorithm uses a lightweight estimator to identify candidate super points and a linear estimator to filter super points. The lightweight estimator is fast, and the linear estimator has high accuracy. Both the lightweight estimator and the linear estimator adopt a data structure, called distance recorder, to support sliding time windows. Moreover, our algorithm is also a parallel algorithm. On the basis of thoroughly discussing the mathematic principles and operation steps of our algorithm, two groups of real-world traffic from a 40-Gb/s high-speed network are applied in the experiments which running on a graphic processing unit (GPU). The experiments are conducted under discrete time windows and sliding time windows separately. The former results show that our algorithm is superior to other existing algorithms in the comprehensive performance, and the latter results indicate that our algorithm can run steadily under sliding time windows.
Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure

Workload modeling in public cloud environments is challenging due to reasons such as infrastructure abstraction, workload heterogeneity and a lack of defined metrics for performance modeling.This paper presents an approach that applies statistical methods for distribution analysis, parameter estimation and Goodness-of-Fit (GoF) tests to develop theoretical (estimated) models of heterogeneous workloads on Amazon's public cloud infrastructure using compute, memory and IO resource utilization data.
Redesigning the Academic Library Materials Budget for the Digital Age: Applying the Power of Faceted Classification to Acquisitions Fund Management

Most academic libraries are facing increasing funding challenges that necessitate improved budget communication and advocacy, in addition to the more traditional planning and monitoring of funds. Moreover, electronic resources continue to evolve rapidly, spawning new material types and modes of acquisition. This paper defines four key facets of a materials budget that has been optimized for the electronic resources environment and describes a process that can be used to redesign any academic library budget structure for the digital age. Specific examples of important practical advantages that have accrued over the six years since the fully-faceted materials budget structure was implemented, are included.
Become an e-ink stained wretch: Build a simple typewriter with a maker-friendly display - [Resources]

My entire professional life as an engineer is spent on, or adjacent to, a glowing laptop. I use the same computer to code, research, design, and document, and even to waste time. Hour upon hour, every day of the week, every week. So I push to have my creative time be analog: Coming home to do some creative writing, only to sit down at the exact same computer, feels more exhausting than restful-oh joy, more screen time. But while I find words as easy to read on paper as on a screen, writing does not translate to the analog world so well. After years of typing on a keyboard, writing in a notebook is slow and hand-crampingly painful. With the relatively recent availability of maker-friendly electronic-paper displays of the sort used by devices like the Amazon Kindle, I fantasized about a device that would bring together the best of both worlds, combining a keyboard with the static, daylight-readable surface of an electronic-paper screen. A device that would make me feel the same about writing as I did about picking up a book, with no eyestrain, notifications, or YouTube distractions. And I could do better than the old dedicated word processors of the 1990s-I'd blast past them with 10 gigabytes of SD card storage, triple the display size, and crisper contrast and custom formatting. However, I quickly found that there were some serious issues I'd have to resolve before banging out a prototype. The first, and most glaring, was the dismal refresh rate of the e-paper screens I was buying- upwards of 2 seconds, if you used the original firmware. If you tried to go faster with custom firmware, you'd get persistent screen burn. It turned out that this was the major reason I hadn't seen my fantasy word processor already on the market-the few similar commercial attempts had received harsh user reviews regarding the lag between pressing a key and seeing a result.
Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts

In this paper we present an application of the specific neural network model for sentiment attitude extraction without handcrafted NLP features implementation. Given a mass-media article with the list of named entities mentioned in it, the task is to extract sentiment relations between these entities. We considered this problem for the whole documents as a three-class machine learning task. The modified architecture of the Convolutional Neural Networks were used and called as Piecewise Convolutional Neural Network (PCNN). The latter exploits positions of named entities in text to emphasize aspects for inner and outer contexts of relation between entities. For the experiments, the RuSentRel corpus was used, it contains Russian analytical texts in the domain of international relations.
A Needs Analysis for the Improvement of the Students’ Communicative Competence; A Syllabus design for Speaking 1 Course at English Department Sawerigading University

Each learner has different needs in learning, as well as the students at the English Department Sawerigading University Makassar. This study aims at determining the effectiveness of a needs analysis to design a syllabus for the speaking 1 course to enhance the students’ communicative competence. Research and Development Methods (R &amp; D) are used with three systematic steps: input, development, and output. The input step begins with a needs analysis (NA) involving 40 respondents who are chosen purposively. The data are collected through NA questionnaires and they are analyzed descriptively. Development Step is made to formulate the objectives and course targets that are then used to determine the course subject of speaking 1. The output step is done to design lesson plans and prototype of teaching materials. This study has found a teaching material development methodology that has resulted lesson plans and prototypes for a unit of instruction-based resource needs. This methodology can be used by designers or lecturers to develop other English teaching materials in various courses.
User Preference-Aware Review Generation

There are more and more online sites that allow users to express their sentiments by writing reviews. Recently, researchers have paid attention to review generation. They generate review text under specific contexts, such as rating, user ID or product ID. The encoder-attention-decoder based methods achieve impressive performance in this task. However, these methods do not consider user preference when generating reviews. Only considering numeric contexts such as user ID or product ID, these methods tend to generate generic and boring reviews, which results in a lack of diversity when generating reviews for different users or products. We propose a user preference-aware review generation model to take account of user preference. User preference reflects the characteristics of the user and has a great impact when the user writes reviews. Specifically, we extract keywords from users' reviews using a score function as user preference. The decoder generates words depending on not only the context vector but also user preference when decoding. Through considering users' preferred words explicitly, we generate diverse reviews. Experiments on a real review dataset from Amazon show that our model outperforms state-of-the-art baselines according to two evaluation metrics.
Research on Modeling and Analysis of Generative Conversational System Based on Optimal Joint Structural and Linguistic Model

Generative conversational systems consisting of a neural network-based structural model and a linguistic model have always been considered to be an attractive area. However, conversational systems tend to generate single-turn responses with a lack of diversity and informativeness. For this reason, the conversational system method is further developed by modeling and analyzing the joint structural and linguistic model, as presented in the paper. Firstly, we establish a novel dual-encoder structural model based on the new Convolutional Neural Network architecture and strengthened attention with intention. It is able to effectively extract the features of variable-length sequences and then mine their deep semantic information. Secondly, a linguistic model combining the maximum mutual information with the foolish punishment mechanism is proposed. Thirdly, the conversational system for the joint structural and linguistic model is observed and discussed. Then, to validate the effectiveness of the proposed method, some different models are tested, evaluated and compared with respect to Response Coherence, Response Diversity, Length of Conversation and Human Evaluation. As these comparative results show, the proposed method is able to effectively improve the response quality of the generative conversational system.
An affordable Web-based Grant Management Software Designed to Support Romanian Scholarly Publications

The rise of Web-based publishing and the increase of electronic usage has triggered profound changes in academic publishing and has determined national governments to take some forms of action designed to actively promote and support the publication of high-quality scientific literature produced in their own country.Considering the spread of ICT technologies nowadays, an online platform was developed in order to encourage participation in the grant competition for Romanian scholarly publications and establish a digital connection between the main actors involved in the process.In this context, the article presents the system architecture, development and outcomes.Building such a platform has required gathering and studying accurate information for data aggregation and user experience, having in mind a scalable design for requirements update or further developments.The system revolves around digitally connecting three major actors -publisher, evaluator, competition organizer.For ensuring the platform's reliability and preventing failures, user-centric tests were developed and set to run automatically at specific triggers, such as code submission.The platform contributes to the transparency of the entire grant management process, substantial reduction of the time for submission, and streamlining of the evaluation procedures.The web platform has been effectively developed, implemented and regularly used by both publishers and reviewers in the latest Romanian grant competitions.
Animal Classification using Deep Learning Inception-v3 Model on Tensor flow

Identification of similar types of objects in image processing now become regular task, but when dissimilar objects come into picture it becomes quite complex.Even it become more complicated when not just we need to identify objects but also categorizes them into their specific classes.We are using machine learning technique to classify the animal and put them into specified classes.Inception-v3 is the open source algorithm made by Google and used for objects classification.In this paper, based on Inception-v3 model in TensorFlow platform, we use the transfer learning technology to retrain the animal category datasets, which can greatly improve the accuracy of animal classification.
Self-Actualization And Formation Of The Civiс Position Of Students Of Pedagogical Specialties

The article dwells on self-actualization as one of the most important processes of forming a holistic personality. It emphasizes the timeliness of the problem under consideration due to the realities of modern civil society. The author provides evidence of the need for in-depth self-study and self-acceptance for an individual’s further development and transformation. The structure and components of a self-actualizing personality are considered, as well as the need to study the mechanisms of the personality formation in the process of educating students. The article presents data of the study of the level of self-actualization among future teachers and acting teachers. Based on the research results, the main difficulties of self-actualization and professional activity of teachers are highlighted. The author makes an attempt to prove the interrelation of the process of forming a student’s civic position with the process of becoming a self-actualizing person. The concept “civic position” is considered in the framework of a comparative analysis. The author analyzes the structural components of the concept “civic position of the individual” and draws parallels in the content of the characteristics of a self-actualizing person and a person with a formed civic position. The possibilities of implementing the selected characteristics through volunteering, students’ participation in competitive professional events and making portfolio in the process of acquiring professional competencies are being explored. As one of the tools for shaping the activity of a person in society, the process of structuring the electronic portfolio of students in the framework of vocational training is considered.
A Hybrid Swarm Optimization Approach for Document Binarization

The binarization process is the preliminary and most significant phase of the document image analysis applications.A hybrid approach based on the merger of Salp swarm algorithm and the chaos theory is introduced.The proposed hybrid approach has been used to evaluate their ability and precision in the clustering process.It is revealed how Salp can operate to find automatically the centroid of a defined number of clusters using K-means objective function.Several different chaotic maps are integrated to adjust the behavior of the Salps by calibrating their random numbers.The efficiency of the proposed chaotic Salp swarm algorithm is empirically verified on the Document Image Binarization Contest H-DIBCO 2016 dataset.A comparison made between the proposed approach and some of the state-of-the-art methods in terms of F-Measure, Peak Signal to Noise Ratio and pseudo-F-Measure.In addition, Geometric-mean pixel accuracy, Distance Reciprocal Distortion Metric, Negative Rate Metric and Misclassification Penalty Metric are shown and discussed.
Using Ternary Rewards to Reason over Knowledge Graphs with Deep Reinforcement Learning.

In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications. We examine the performance metrics used by state-of-the-art systems and determine that they are inadequate for such settings. More specifically, they do not evaluate the systems correctly for situations when there is no answer available and thus agents optimized for these metrics are poor at modeling confidence. We introduce a simple new performance metric for evaluating question-answering agents that is more representative of practical usage conditions, and optimize for this metric by extending the binary reward structure used in prior work to a ternary reward structure which also rewards an agent for not answering a question rather than giving an incorrect answer. We show that this can drastically improve the precision of answered questions while only not answering a limited number of previously correctly answered questions. Employing a supervised learning strategy using depth-first-search paths to bootstrap the reinforcement learning algorithm further improves performance.
Extracting Keyphrases from Research Papers Using Word Embeddings

Unsupervised random-walk keyphrase extraction models mainly rely on global structural information of the word graph, with nodes representing candidate words and edges capturing the co-occurrence information between candidate words. However, integrating different types of useful information into the representation learning process to help better extract keyphrases is relatively unexplored. In this paper, we propose a random-walk method to extract keyphrases using word embeddings. Specifically, we first design a new word embedding learning model to integrate local context information of the word graph (i.e., the local word collocation patterns) with some crucial features of candidate words and edges. Then, a novel random-walk ranking model is designed to extract keyphrases by leveraging such word embeddings. Experimental results show that our approach outperforms 8 state-of-the-art unsupervised methods on two real datasets consistently for keyphrase extraction.