Reviewerly/exHarmony huggingface dataset:
Com'è stata scritta la letteratura italiana: per un libro di Armando Petrucci

The author reviews Armando Petrucci's book Letteratura italiana: una storia attraverso la scrittura (Roma, Carocci, 2017, pp. 728), which collects twenty essays written by the great paleographer and historian of writing from 1965 to 2001, and illustrates his fundamental contribution to Italian studies, with particular reference to the mediaeval period.
An Examination of the Effects of Noise Level on Methods to Determine Curvature in Range Images

A study of the impact of image noise on well-known range image curvature determination methods is presented here. The study considers 12 methods, and each is analyzed based on its performance at varying levels of input noise. The performance analyses consider quality factors of (1) absolute error, (2) correlation with correct, expected curvature values, and (3) signal-tonoise ratio (SNR). Curvature-based renderings are also presented for some data to provide basic visualizations of the impact of noise on one curvature-based task. The work can benefit tasks using range data (e.g., from Kinect or commercial-grade sensors).
공익 광고를 활용한 한국어 중급 학습자의 복합 문식성 교육 연구

This study aimed to enhance multiliteracies of intermediate learners who learn Korean by selecting appropriate teaching-learning contents and methods with the help of public advertisements. In addition, the research applied hypothetical measures to actual learners and analyzed the subjects in terms of multiliteracies. The public advertisements had two distinctive themes. One theme contained universal values which are admittable both in Korea, the target culture and also in learners'' mother culture. Another theme contained unique sentiments of Korea. The model for teaching and learning multiliteracies by using public advertisement was presented in three stages: conceptualization phase, expansion phase, and application phase. In the research, multiliteracies of the learners were evaluated based on the UCCs that they have made during the public advertisement-related teaching-learning session, and they were classified as follows: Critical implementation of universal themes and expressed their meanings focusing on the cultural gaps that were shown in the public advertisements. Through the result, it was verified that public advertisements have educational effects on the enhancement of multiliteracies. Rather than just understanding the superficial meanings of the textbook, public advertisements helped learners understand complex forms including movies, sounds and music and further create new senses, the ability so-called ''multiliteracies''.(Sungkyunkwan University · Ewha Womans University)
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, illumination, etc. An attention convolutional binary neural tree architecture is presented to address those problems for weakly supervised FGVC. Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree. The final decision is computed as the summation of the predictions from leaf nodes. The deep convolutional operations learn to capture the representations of objects, and the tree structure characterizes the coarse-to-fine hierarchical feature learning process. In addition, we use the attention transformer module to enforce the network to capture discriminative features. The negative log-likelihood loss is used to train the entire network in an end-to-end fashion by SGD with back-propagation. Several experiments on the CUB-200-2011, Stanford Cars and Aircraft datasets demonstrate that the proposed method performs favorably against the state-of-the-arts.
The Paleoproterozoic Mcarthur River (Hyc) Zn/Pb Deposit and Controversial Hypotheses of its Origin

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Data Ordering Patterns for Neural Machine Translation: An Empirical Study

Recent works show that ordering of the training data affects the model performance for Neural Machine Translation. Several approaches involving dynamic data ordering and data sharding based on curriculum learning have been analysed for the their performance gains and faster convergence. In this work we propose to empirically study several ordering approaches for the training data based on different metrics and evaluate their impact on the model performance. Results from our study show that pre-fixing the ordering of the training data based on perplexity scores from a pre-trained model performs the best and outperforms the default approach of randomly shuffling the training data every epoch.
Shelf Life Study of Calibration Stable Isotope Hydrocarbon Mixtures for Petroleum Geochemistry Laboratories

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블렌디드 러닝을 활용한 대학의 무용 교수학습내용 구성에 관한 연구

Online education using smart devices is being introduced in many areas as it removes limits of spatiotemporal identity of professors and learners, but there are limitations by the absence of personal meeting between professors and learners. In order to overcome this limitation, blended learning that combined benefits of online and offline education is being expanded and used in the whole field of education.There are concerns about the declining quality of education as competition became intensified by ranking of universities due to the advent of depopulation era and as they are being operated around economic aspects.At this stage of the existence of university dance education getting threatened by a rearrangement issue of dance department, studies on economical dance teaching-learning contents using blended learning with high educational effect are being required.This study developed a dance teaching-learning contents as a related course of dance education and dance technique(ballet) using combined blended learning.Lecture contents of dance education is organized into the general term of 15 weeks, each lecture will be conducted twice a week for 50 minutes, and smart learning with the same percentage of time is composed of 20-minute video by considering the concentration level of learners. Also, professors can check the understanding of learners through a quiz provided with the video lecture in advance, and provide a mini lecture when additional explanation is needed. For an offline lecture, the positive effect of blended learning can increase by expanding the range of character education with problem based learning, cooperative learning, and peer learning through discussion learning.Lecture contents and general curriculum of performance related subject are the same as teaching-learning contents of dance education subject, but secured time for experience learning by lowering a considerable portion of smart learning. This is a dance teaching-learning contents with integrated nature by combining lectures of practical technique with contents of related dance history and creation subject. Learners can participate in discussions based on understanding of the trend of dancing through smart learning, and can experience an increase in awareness through artistic expression and experience learning by learning characteristics of movements by period.The development of dance teaching-learning contents using blended learning, as a blended education model for cultivating future integrated human resources with humanistic imagination, will effectively contribute to the cultivation of dance professionals integrated human resources with integrated thinking suitable for the ICT period through the combination of theory and practical skill and converged subjects. It will contribute to producing dance professionals who can perform in various fields.
Pengaruh Penerapan Model Pembelajaran Discovery Learning dan Aktivitas Belajar terhadap Hasil Belajar Biologi pada Siswa Kelas VII SMP Muhammadiyah 10 Surakarta 2018/2019

The aim of the research were to find out and analyze: (1) the effect of discovery learning model on students’ learning outcome in Biology; (2) the effect of students’ learning activities on students’ learning outcome in Biology; and (3) the interaction effect of learning model and students’ activities on students’ learning outcome in Biology at class VII students of SMP Muhammadiyah 10 Surakarta academic year 2018/2019. The research is an experimental study with the subject of class VII students of SMP Muhammadiyah 10 Surakarta academic year 2018/2019. The data analysis is done by employing ANCOVA using uneven 2 X 3 cells. The research concluded that: (1) there is a significant effect of discovery learning model on students’ learning outcome in Biology. It is indicated with the Fstatistic result of 6.045 that significant at 0.005; (2) there is a significant effect of students’ learning activities on students’ learning outcome in Biology. It is indicated with the Fstatistic result of 5.606 that significant at 0.005; and (3) there is a significant interaction effect of learning model and students’ activities on students’ learning outcome in Biology at class VII students of SMP Muhammadiyah 10 Surakarta academic year 2018/2019. It is indicated with the Fstatistic result of 3.354 that significant at 0.005.
Meta-Feature Engineering for Machine Learning-Based Automated Data Visualization

41 CFR Parts 60-1 through 60-60, 60-250 ,and 60-741.
Segmentation of Punjabi Text into Prosodic Unit

Naturalness of synthesized speech is defined in terms of accurate pronunciation of words, whether the stress is given to the right word and are the nasal sound words are spoken properly. It is the task of text analysis phase of speech synthesizer to generate linguistic representations of written text by considering nasality, stress and pronunciation of words. As there is difference in written and verbalization form of text. So, text analyser performs text normalization i.e. some preprocessing on written text to transform text into its verbalization form. Some pre-processing tasks performed are date expansion, numeral expansion, alphanumeric expansion, abbreviation expansion, and phonological translation. The vowel-consonant sequence of normalized text is generated that is used to guide the process of segmenting the text into prosodic units (graphemes) and generation the linguistic representation of text.
AN EFFICIENT TRUST COMPUTING SCHEME FOR IMPLEMENTING A TRUSTWORTHY AND COLLABORATIVE CLOUD SERVICE

The vital task of cloud computing programme is to provide trusted services to users. The user wants to deliver their critical tasks and information to cloud data centre. So that, the burden of the cloud customer is shifted to cloud data centre. Trust has a prime role between user and cloud service provider. There are many approaches available to calculate trust value in collaborative cloud computing. In the proposed system, to provide security, data uploaded to cloud will be in encrypted format. Parallel trust computing scheme will calculate the trust. A thread will be created for each user request and will be fast as it has to evaluate the trust for single user. Trust will be determined based on successful login, and response time. The response time calculated from each request is added to matrix window, from which we can predict and recommend the best performing cloud services to user. The experimental result support practicality and success of the proposed system.
RECENT AGILE REQUIREMENT ENGINEERING PRACTICES IN IT PROJECTS: A CASE ANALYSIS

Today, the implementation of high quality and efficient Requirement Engineering (RE) practices in agile software development projects, is gaining great importance. Practitioners and researchers seeks for lighter RE practices that can handle the issues of abstract, unclear and changing requirements, and at the same time that can satisfy the Agile Manifesto philosophy. This study examines importance of RE practices in agile software development projects, and explores which aspects of the RE practices are perceived as most critical and how such aspects are adapted in practice today through two different agile software development projects of a case organization. This study aims to contribute agile RE literature by providing an interpretive analysis on perception of agile RE practices from different perspectives (agile team members, product owners, some top executives). Within this context, this study draws lessons from case studies and presents beneficial agile RE guidelines for practitioners and researchers.
Modeling Relationship Maps for The Factors of Purchasing Management System According to Selected Key Drivers Using Fuzzy Cognitive Maps

Strategic management is the philosophy of the creation which provides a goal, a plan and a model for the competitive advantage. Companies define their own goals and generate the plan for goal according to this management philosophy. However, company can utilize successively from this philosophy so long as it is able to be adapted to all management systems such as product, inventory, manufacturing and etc... Purchasing management system is the one of them. It is a basic management unit where decisions, goals and plans are structured according to production and customer requirements which give the competitive advantage to each company. All of them can be managed strategically by being focused on the appropriate purchasing conditions including low-cost, high quality, and on-time delivery. In this study, we will determine variables and the relationships between them which affect in achieving the strategies of the company in terms of purchasing management system. Fuzzy cognitive mapping method will be selected in order to find them out because this method creates a map by using some strategies for system. The results will be presented at the end of the study.
Multi-grained Attention Networks for Single Image Super-Resolution

Deep Convolutional Neural Networks (CNN) have drawn great attention in image super-resolution (SR). Recently, visual attention mechanism, which exploits both of the feature importance and contextual cues, has been introduced to image SR and proves to be effective to improve CNN-based SR performance. In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts. We further propose a unified approach called "multi-grained attention networks (MGAN)" which fully exploits the advantages of multi-scale and attention mechanisms in SR tasks. In our method, the importance of each neuron is computed according to its surrounding regions in a multi-grained fashion and then is used to adaptively re-scale the feature responses. More importantly, the "channel attention" and "spatial attention" strategies in previous methods can be essentially considered as two special cases of our method. We also introduce multi-scale dense connections to extract the image features at multiple scales and capture the features of different layers through dense skip connections. Ablation studies on benchmark datasets demonstrate the effectiveness of our method. In comparison with other state-of-the-art SR methods, our method shows the superiority in terms of both accuracy and model size.
Asymptotic Behavior and Limiting Distribution of Quantum Walk on Cycles with General U(2) Coin by Reduced Characteristic Matrix Method

We calculated reduced density characteristic matrix (RDCM) for quantum walk on cycles (QWC) to study asymptotic properties of the most general form of quantum walk on cycles with general $U(2)$ coin operator. As an example, entanglement temperature for general initial state has been calculated and compared to previous results. Also, we have modified RDCM to derive analytical expression for general form of limiting distribution (LD).
Information‐based Requirement Development &amp; Management

INCOSE International SymposiumVolume 29, Issue 1 p. 248-248 Session 4 Track 1: Requirements 1 Information-based Requirement Development & Management Mike Ryan, Mike Ryan The University of New South WalesSearch for more papers by this authorJuan Llorens, Juan Llorens Universidad Carlos III de MadridSearch for more papers by this authorLouis Wheatcraft, Louis Wheatcraft Requirements Experts/SeilevelSearch for more papers by this authorJeremy Dick, Jeremy Dick SyntheSys System Engineers LtdSearch for more papers by this author Mike Ryan, Mike Ryan The University of New South WalesSearch for more papers by this authorJuan Llorens, Juan Llorens Universidad Carlos III de MadridSearch for more papers by this authorLouis Wheatcraft, Louis Wheatcraft Requirements Experts/SeilevelSearch for more papers by this authorJeremy Dick, Jeremy Dick SyntheSys System Engineers LtdSearch for more papers by this author First published: 27 September 2019 https://doi.org/10.1002/j.2334-5837.2019.00601.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume29, Issue1July 2019Pages 248-248 RelatedInformation
Ensemble Spatio-Temporal Distance Net for Skeleton Based Action Recognition

The performance of an efficient and accurate action recognition system heavily depends on distinctive representations for a different class of action sequences. To address this issue, we propose an ensemble network in this paper. We design two multilayer Long Short Term Memory networks to capture spatial and temporal dynamics of the entire sequence, referred to as Spatial-distance Net (SdNet) and Temporal-distance Net (TdNet) respectively. More specifically, SdNet captures the spatial dynamics of joints within a frame and TdNet explores the temporal dynamics of joints between frames along the sequence. Finally, two nets are fused as one Ensemble network, referred to as Spatio -Temporal distance Net (STdNet) to explore both spatial and temporal dynamics. The efficacy of the proposed method is evaluated on two widely used datasets, UTD MHAD and NTU RGB+D, and the proposed STdNet achieved 91.16% and 80.03% accuracies respectively.
Semantic Deviation in Classical Poem Interpretation: A Perspective from Beginning Training Course Students in TEFL

Manipulation of language used in poems to state poet’s idea is basically aimed to give aesthetic value, help the readers to easily understand, and give complete image of the text meaning. However, in a real application in the classroom, the language style in poems causes language deviation and covers the poet’s idea. Readers should read the poems explisitly in deep interpretation to get the meaning. Therefore, this study describes an eight-week (135 minutes a week) poem stylistics of semantic deviation analysis for a half –semester beginning training course (poetry class) for the use of literature in an EFL context. The research was conducted at the fourth semester students of English Program of IAIN Bengkulu in academic year 2017/2018 that consisted of 31 students. This descriptive analysis aimed to describe students’ exploration of semantic deviation in classical poem interpretation. The result showed that Wordsworth’s The Solitary Reaper used various kinds of semantic deviation in forms of language stylistics of figurative languages, namely: (1) semantic Bizarreness of expression that consisted of oxymoron and paradox; (2) transference of meaning that consisted of synecdoche, metonymy and metaphor; (3) Misrepresent the truth that consisted of hyperbole and irony. In a real analysis of the poem, the students were excellent in identyfying the kinds of language deviations. Then, they were very good at understanding the poem literally. However, they had problems in interpreting the poet’s real meaning through semantic deviation used in the poem because they needed more experience about lingistic, social and historical background knowledge of classical England, and biography of the poet.
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations has been investigated. In this work, we further advocate that handling uncommon entities is inevitable when dealing with infrequent relations. Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions. We design a novel model to better extract key information from textual descriptions. Besides, we also develop a novel generative model in our framework to enhance the performance by generating extra triplets during the training stage. Experiments are conducted on two datasets from real-world KGs, and the results show that our framework outperforms previous methods when dealing with infrequent relations and their accompanying uncommon entities.
Automatic Text Summarization Techniques Used in Industry

Automatic text summarization has very efficient techniques to create the summarization (important information form document) of the text document. As we know the information on the internet is growing rapidly and we cannot go through all the documents on the internet. If we want to read the news we have to identify which news we have to read then the news article can be summarized in only one paragraph by information extraction from the news article and by reading this short paragraph we can identify which article we should read. The automatic text summarization can be done by three methods. First one is the extraction method in which we just find the informative sentences from the whole document text. The second approach is the abstraction technique in which we find the meaning of the sentences and try to formulate the paragraph in the same meaning manner or we can say the same thing express the fewer words. The third and last approach is compression technique in which compress each sentence of the document. We compress the sentence by having the words related to the meaning of the sentence by title or first sentence of the graph. We can apply the hybrid approach to create the summarization in which we first compress the whole text document and then apply knowledge extraction. Knowledge extraction can be done by giving the score to each sentence and then pick the best k sentences according to compression ratio.