Reviewerly/exHarmony huggingface dataset:
Three states of wax

Three States of Wax is a performance environment for an ensemble of computer-extended instruments. It investigates the nature of musical materials in a live computational environment. Three States of Wax develops ideas from Michel Serres’ early work on the materials of physics. He takes as his example a piece of wax: it has form or figure, however malleable, it can be understood on a molecular level and in terms of the physical laws of propagation, and it bears the traces of its own history – of the feeding habits of the bees, of temperature, of intentional or circumstantial forming. Serres proposes that the interference between different models, theories or modes of representation is the engine of knowledge. A central computer monitors interaction and the dynamics and evolution of the whole, identifying emergent phenomena. The differences between various modes of representation determine its responses of amplifying, inhibiting or contradicting the live behaviours.
Data Visualisation in Midwifery: The Challenge of Seeing what Datasets Hide.

Information visualisation is transforming data into visual representations to convey information hidden within large datasets. Information visualisation in medicine is underdeveloped. In midwifery, the impact of different graphs on clinicians' and patients' understanding is not well understood. We investigate this gap and its potential consequences.
Fashion Virtual Reality in E-commerce

The Virtual Reality (VR) and Augmented Reality (AR) economic impact is expected to hit US$ 29.5 billion in 2020. Brands (Volvo / L'Oreal) and retailers (Carrefour / Lowe's) have built on-site VR facilities which offer more attractive shopping experiences than traditional environments. The restricted and inconclusive results of the retail hunt, however, call for further studies on how to create more productive online shopping environments. There are some types of VR and so many applications for it in the Fashion Industry, as interactive mirrors and glasses or apps and webs to feel like if you were shopping and trying all the clothes without moving of the sofa. Online purchase is expected to be more secure because the consumer can virtually try outfits before buying. This paper analyses the consumers experience using an experiment with female undergraduate students. The experiment was conducted to determine the feasibility of using VR fashion retail store models in designing store layouts and understanding how shoppers communicate with shops.
The Coupled Evolution of Temperature and Carbonate Chemistry during the Paleocene–Eocene;&#x0D;New Orbital-Resolution Trace Metal Records from the Low-Latitude Indian Ocean

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]The Coupled Evolution of Temperature and Carbonate Chemistry during the Paleocene–Eocene; New Orbital-Resolution Trace Metal Records from the Low-Latitude Indian OceanAuthors James Barnet Kate Littler Dustin Harper Leah LeVay iD Kirsty Edgar iD Tali Babila iD Clemens Ullmann Melanie Leng Dick Kroon James Zachos iDSee all authors James BarnetCorresponding Author• Submitting AuthorUniversity of ExeterUniversity of St Andrewsview email addressThe email was not providedcopy email addressKate LittlerUniversity of Exeterview email addressThe email was not providedcopy email addressDustin HarperUniversity of California Santa CruzThe University of Kansasview email addressThe email was not providedcopy email addressLeah LeVayiDTexas A&M UniversityiDhttps://orcid.org/0000-0003-3031-7079view email addressThe email was not providedcopy email addressKirsty EdgariDUniversity of BirminghamiDhttps://orcid.org/0000-0001-7587-9951view email addressThe email was not providedcopy email addressTali BabilaiDUniversity of California Santa CruzUniversity of SouthamptoniDhttps://orcid.org/0000-0001-9948-9341view email addressThe email was not providedcopy email addressClemens UllmannUniversity of Exeterview email addressThe email was not providedcopy email addressMelanie LengBritish Geological SurveyUniversity of Nottinghamview email addressThe email was not providedcopy email addressDick KroonUniversity of Edinburghview email addressThe email was not providedcopy email addressJames ZachosiDUniversity of California Santa CruziDhttps://orcid.org/0000-0001-8439-1886view email addressThe email was not providedcopy email address
Teachers' Perception Of Developing Problem Solving Skills In Primary Education

Recommendation of the European Parliament and of the Council from 18 December 2006, on key competences for lifelong learning urges EU governments to include teaching and learning of key competences in their lifelong learning strategies. The recommendation identifies eight key competencies that are fundamental to each person within a knowledge-based society. In an increasingly globalized world, people need a wide range of skills to adapt and thrive in a rapidly changing environment. The original lifelong learning program was designed to provide people with learning opportunities at all stages of life. The work is based on the idea that problem solving is part of the thinking process and is considered as the most complex of all intellectual functions, being considered a higher order cognitive process. It occurs when the body becomes stuck and does not know how to proceed from the current state to the desired state. It is part of a larger process, which includes identifying a problem and formulating it. Therefore, in school it is absolutely necessary to pay more attention to the process of developing problem solving skills, within projects and beyond. The purpose of our research was to discuss with the teachers and to analyze their experiences regarding their own ways of guiding the development of the resolving skills for the students in the primary education.
KESANTUNAN BERBAHASA DOSEN-MAHASISWA DALAM INTERAKSI PERKULIAHAN DI IAIN TULUNGAGUNG

Politeness plays an important role in interaction in order to ovoid friction and conflict between speaker and hearer. This article aims to describe politeness by lecturer to students in academic interaction at IAIN Tulungagung. This study is qualitatively approached using pracmatics design. The data were collected using technique of observation by listening, writing notes, and participating. The data then, analyzed using interactive model adopted from Miles & Huberman. The data were classified according to face theory of Brawn & Levinson i.e. strategy of positive politeness and strategy of netagive politeness. Data analysis reveals that positive politeness strategies used by lecturer in academic interaction at IAIN Tulungagung are (1) the use of attention to speaker need, (2) the use group identity markers, (3) the use of agreement on the same topics, (4) the use of ovoiding disagreement by pseudo-agreement, (5) expressing understanding of speaker need, (6) the use of being optimistic, (7) involving the hearer in the speaker activities, (8) seeking for reasons or giving question, and (9) extending presents. While negative politeness strtaegies used by lecturer in academic interaction at IAIN Tulungagung are (1) the use of indirect utterances, (2) the use of being pessimistic, (3) minimize burden, (4) the use of respect, (5) asking for apologies, (6) the use of impersonal form, (7) the use of general rules of interaction.
Web 2.0 PBN Network 10x+ Sites Manually Created

Powerup your websites with powerful web 2.0 network websites and build your domain authority and skyrocket up the serps. We manually create these sites which are great ways to pass along juice and sometimes considered buffer sites.
A Segmentation Network with a Class-Agnostic Loss Function for Training on Incomplete Data

The use of deep learning methods is increasing in medical image analysis, e.g., segmentation of organs in medical images. Deep learning methods are highly dependent on a large amount of training da ...
RSCI as a Mirror of Publication Activity of RAE Members

Based on information from open sources, a table was compiled reflecting the indicators of 128 full members of the Russian Academy of Education (RAE) in the Russian Science Citation Index (RSCI). The main results are given in a condensed form and compared with the results of a similar study performed several years earlier. The conclusions and features of the RSCI as an analytical tool are discussed.
An Exact Transformation from Deep Neural Networks to Multi-Class Multivariate Decision Trees

Artificial neural networks (ANNs) are commonly labelled as black-boxes, lacking interpretability. This hinders human understanding of ANNs' behaviors. A need exists to generate a meaningful sequential logic for the production of a specific output. Decision trees exhibit better interpretability and expressive power due to their representation language and the existence of efficient algorithms to generate rules. Growing a decision tree based on the available data could produce larger than necessary trees or trees that do not generalise well. In this paper, we introduce two novel multivariate decision tree (MDT) algorithms for rule extraction from an ANN: an Exact-Convertible Decision Tree (EC-DT) and an Extended C-Net algorithm to transform a neural network with Rectified Linear Unit activation functions into a representative tree which can be used to extract multivariate rules for reasoning. While the EC-DT translates the ANN in a layer-wise manner to represent exactly the decision boundaries implicitlylearned by the hidden layers of the network, the Extended C-Net inherits the decompositional approach from EC-DT and combines with a C5 tree learning algorithm to construct the decision rules. The results suggest that while EC-DT is superior in preserving the structure and the accuracy of ANN, Extended C-Net generates the most compact and highly effective trees from ANN. Both proposed MDT algorithms generate rules including combinations of multiple attributes for precise interpretation of decision-making processes.
THE IMPACT OF FOREIGN PORTFOLIO INVESTMENT ON THE NIGERIA STOCK MARKET: (1980 – 2016)

This study examines the impact of foreign portfolio Investment (FPI) on the Nigerian Stock market for the period of 36 years (1980 – 2016). The major objective of the study is to establish empirically whether foreign portfolio investment contributes to the growth of   the Nigerian stock market capitalization and thus the growth of the Nigerian economy. The ordinary least square (OLS) method and the parsimonious error correction model specification were used in the study. The Augmented Dickey Fuller unit root test was used to test the stationary status of the variables before carrying out co-integration regression which shows non-stationarity of the variables. The co-integration test finds that the variables co-integrate. The Parsimonious error correction mechanism was used to estimate the variables and that all the variables were found to be significant in explaining 84% of the variation in the dependent variable (Market capitalization). The result shows that foreign portfolio investment has a positive impact on capital market growth with the speed of adjustment from short run to long run as indicated by the ECM-1 having a relatively high value of 34% in absolute terms and there is a strong relationship between FPI and stock market growth and domestic savings are complementary and not substitute to the growth of the Nigerian stock market in both the short run and the long run .  The study concluded that the presence of foreign portfolio investment has increased the market capitalization of the Nigeria stock market. FPI has grown recently in proportion to other types of capital flow in Nigeria because it complements domestic savings to get the desired level of investment. It recommended that authorities and policy makers should come up with policies that are more investment friendly, furthermore, the policy of internationalization of stock market operations should be sustained as these would boost domestic investment.
On some classes of irreducible polynomials

One of the fundamental tasks of Symbolic Computation is the factorization of polynomials into irreducible factors. The aim of the paper is to produce new families of irreducible polynomials, generalizing previous results in the area. One example of our general result is that for a near-separated polynomial, i.e., polynomials of the form F(x,y)=f1(x)f2(y)−f2(x)f1(y), then F(x,y)+r is always irreducible for any constant r different from zero. We also provide the biggest known family of HIP polynomials in several variables. These are polynomials p(x1,…,xn)∈K[x1,…,xn] over a zero characteristic field K such that p(h1(x1),…,hn(xn)) is irreducible over K for every n-tuple h1(x1),…,hn(xn) of non constant one variable polynomials over K. The results can also be applied to fields of positive characteristic, with some modifications.
Guides: VIP Guide for Students: Tips & Best Practices

This guides provides some tips, best practices, time management strategies (including working from home with kids), ideas for staying healthy, and some suggestions for tools and technology.
Cloud Computing: A Revolution in Libraries and Information Services

Cloud computing technology has changed the scenario of the world. During last decades this technology has been adopted by all over the world and it has changed the scenario of the way you working. Cloud computing technology has made many things possible which may not possible in the traditional computing methods. There are many advantages of cloud computing for libraries also, many libraries are now moving into the cloud, it has reduced the heavy burden of library staff to protect and preserve their digital data and assets. Through consortia libraries can create a network of libraries and sharing their resources with member libraries. In this article, we have discussed concept, types, service models, advantages and limitations of cloud computing.
Cost-Sensitive Trees for Energy-Efficient Context Recognition

An important problem in the field of context recognition is preserving the battery life of the sensing device. In this work we introduce cost-sensitive learning that takes into account the cost of having the sensors active and tries to balance it against the classification error. The benefits can be amplified by adapting the classifier to each context. This approach was tested on two real-life datasets where we achieved 78% and 80% reduction in energy consumption in exchange for almost no accuracy loss.
LibGuides: Academic Resources for Teens: Other resources available

A guide designed for teens to understand how to navigate the database Explora and how to use the information to their advantage
Privacy Based Preservation in Multi Cloud Environment Using Attribute Level Trusted Access Measure Technique

The Portent of data security in cloud setting is well studied. There are number of approaches has been discussed for the data security in multi cloud environments. The previous methods suffer to achieve higher performance in data security and privacy preservation. To improve the performance, an attribute level trusted access measure is presented. The method classifies the user data into several classes and for each class of attributes different access restriction has been enforced. The access between different cloud has been restricted based on the attribute level trusted access measure (ALTAM). The ALTAM measure for each request has been estimated based on the depth of privacy has been accessed by the request and the number of data values the user has access in the cloud and outside the cloud. Based on all these the method estimates the trust measure. According to the trust measure, the method restrict the access of data in multi cloud environments. The method produces efficient result in access restriction and privacy preservation.
Dual Preference Matrix Collaborative Filtering Algorithm and Its Application in Railway B2BE-commerce Platform

The enterprise users of the B2B e-commerce platform are different from the individual users of B2C, and their behavior has typical features of enterprise. Based on the original user-commodity matrix of collaborative filtering algorithm, the enterprise-category matrix which manifests the features of enterprise users is added to ameliorate and form a dual preference matrix concerning both user-commodity and enterprise-category relationship, thus can be applied to Railway B2B E-commerce platform. The experimental results indicate that the improved dual-preference matrix collaborative filtering algorithm has advantages over the original collaborative filtering algorithm and KNN algorithm in terms of accuracy, recall rate, coverage rate and degree of novelty, effectively solving the problem of cold start and improves the accuracy of recommending commodities towards inactive users by over 10%.
Identifiera löv i skogar – Att lära en dator känna igen löv med ImageAI

A current field of research today is machine learning because it can simplify everyday life for human beings. A functioning system that has learned specific tasks can make it easier for companies i ...
LibGuides: Double Bass Studio: Search tips for locating scores & recordings

This guide is designed to help students of the double bass locate resources for research and study.
Validation of Smart Contracts Using Process Mining

Smart contracts are self-executing contracts defining rules for negotiating, verifying the fulfillment of rules and executing the agreement using formal code. They run on top of a blockchain. Errors in smart contracts are costly and are mostly found too late after execution, which is too late for fixing. To improve the validation of executed smart contracts, this paper suggests a process mining based approach. For this, we present an approach for the extraction of meaningful event logs from a blockchain. The event log can be imported in any process mining tool and validation and verification techniques can be used allowing to diagnose (non)conformity in smart contracts by means of common quality measures and with low latency after smart contract execution.
Problems of Formation of Professional Competence in Future Teachers

Competence is a relative concept: the system is developing - the requirements for the competence of employees are changing. In this regard, competency management should be considered as a powerful factor in increasing the competitiveness of the enterprise.
De-Aliasing States In Dialogue Modelling With Inverse Reinforcement Learning

End-to-end dialogue response generation models learn dialogue state tracking, dialogue management, and natural language generation at the same time and through the same training signal. These models scale better than traditional modular architectures as they do not require much annotation. Despite significant advances, these models, often built using Recurrent Neural Networks (RNNs), exhibit deficiencies such as repetition, inconsistency, and low task-completion rates. To understand some of these issues more deeply, this paper investigates the representations learned by RNNs trained on dialogue data. We highlight the problem of state aliasing, which entails conflating two or more distinct states in the representation space. We show empirically that state aliasing often occurs when encoder-decoder RNNs are trained via maximum likelihood or policy gradients. We propose to augment the training signal with information about the future to force the latent representations of the RNNs to hold sufficient information for predicting the future. Specifically, we train encoder-decoder RNNs to predict both the next utterance as well as a feature vector that represents the expected dialogue future. We draw inspiration from the Structured-Classification Inverse Reinforcement Learning (SCIRL, Klein et al., 2012) algorithm to compute this feature vector. In experiments on a generated dataset of text-based games, the augmented training signal mitigates state aliasing and improves model performance significantly.
Author Identification: Performance Comparison using English and Under-Resourced Languages

Abstract This paper presents Author Identification (AI) task using different language which are English and Under-Resourced Languages (U-RL) (i.e. KadazanDusun and Iban). In this paper, the performance of AI task is analysed using English and the U-RL datasets in terms of accuracy. Different stylometric features and emerging machine learning algorithms (i.e. SVM and Random Forest) are examined to obtain optimal results in AI task. The approach used in AI task is based on supervised machine learning. Cross-validation is used to evaluate the performance of AI task. The findings include the performance comparison of different stylometric feature and classifiers between the three datasets based on their accuracy values. The combination of word n-grams with character 3-grams achieved the highest accuracy with almost 75% using English dataset. For classifier, SVM gained better result for all three datasets compared to Random Forest.
INFORMATION RETRIEVAL FROM MULTI DOMAIN SPECIFIC RESEARCH PROPOSAL USING HIERARCHICAL BASED NEURAL NETWORK CLUSTERING ALGORITHM

Research project selection is an essential task for government and private agencies. When a huge number of research proposals are received, it is common to group them along with their similarities in research discipline areas. In this paper, a new framework is proposed to assign the proposal to reviewer and identify the proposal theme automatically. First, a list of multi word terms are extracted automatically from the domain specific corpus of text documents into a hierarchy based on their semantic similarity. Second, the proximity of two vectors can be calculated using measures such as probabilistic correlation. The conditional probability is used to model the probability that a term appear together in a record. Third, a hierarchical-based neural network clustering algorithm is used to get an information retrieval from multi-domain specific research proposal.
Mobile Service Selection

Service selection has always been a hot topic in services computing area. In mobile environment, the characteristics such as mobility, unpredictability and variation of the signal strength of mobile networks bring great challenges for the selection of optimal services. Traditional QoS-aware methods selecting services with the best QoS may not always result in a best composition as the constant mobility makes the performance of service invocation unpredictable and location-based. Moreover, due to the limits of the battery capacity of all mobile devices, how to select cloud services in order to reduce energy consumption in mobile environments is becoming a critical issue. This chapter addresses the problem of mobile service selection for composition in terms of QoS and energy consumption and proposes the resolving methods respectively.
Evaluating German Transformer Language Models with Syntactic Agreement Tests.

Pre-trained transformer language models (TLMs) have recently refashioned natural language processing (NLP): Most state-of-the-art NLP models now operate on top of TLMs to benefit from contextualization and knowledge induction. To explain their success, the scientific community conducted numerous analyses. Besides other methods, syntactic agreement tests were utilized to analyse TLMs. Most of the studies were conducted for the English language, however. In this work, we analyse German TLMs. To this end, we design numerous agreement tasks, some of which consider peculiarities of the German language. Our experimental results show that state-of-the-art German TLMs generally perform well on agreement tasks, but we also identify and discuss syntactic structures that push them to their limits.