Psalter: Number 42

Ink is smudged and the vellum has darkened.; Angular Gothic Script; This leaf is the largest in the collection. It is done in a very simple style. The musical notation is in a free melody style, the horseshoe notation indicates all the notes that is acceptable to sing.; Black, red, and blue inkshttps://digital.kenyon.edu/mdvlmanuscripts/1019/thumbnail.jp

Evaluation de la performance financière des banques participatives au Maroc : Une analyse par le modèle CAMEL

La présente étude analyse la performance des banques participatives opérant au Maroc sur une période allant de 2018 à 2020 en utilisant le modèle de notation CAMEL adopté par trois superviseurs bancaires fédéraux des États-Unis vers la fin des années soixante-dix. La recherche vise à évaluer les cinq composantes du modèle CAMEL : Adéquation des fonds propres, qualité des actifs, qualité de gestion, profitabilité, liquidité. L’échantillon porte sur cinq banques participatives marocaines. Les résultats de cette étude montrent que toutes les banques participatives ont une forte notation pour la composante adéquation du capital et la composante qualité des actifs et une faible notation pour le reste des composantes à savoir la qualité de gestion, la profitabilité et la liquidité. Cependant, toutes les banques participatives ont eu un score CAMEL composite égal à (3,2) avec un statut « équitable ». En effet, la performance des banques participatives est acceptable avec certains risques associés

Handwritten Music Recognition for Mensural notation with convolutional recurrent neural networks

[EN] Optical Music Recognition is the technology that allows computers to read music notation, which is also referred to as Handwritten Music Recognition when it is applied over handwritten notation. This technology aims at efficiently transcribing written music into a representation that can be further processed by a computer. This is of special interest to transcribe the large amount of music written in early notations, such as the Mensural notation, since they represent largely unexplored heritage for the musicological community. Traditional approaches to this problem are based on complex strategies with many explicit rules that only work for one particular type of manuscript. Machine learning approaches offer the promise of generalizable solutions, based on learning from just labelled examples. However, previous research has not achieved sufficiently acceptable results for handwritten Mensural notation. In this work we propose the use of deep neural networks, namely convolutional recurrent neural networks, which have proved effective in other similar domains such as handwritten text recognition. Our experimental results achieve, for the first time, recognition results that can be considered effective for transcribing handwritten Mensural notation, decreasing the symbol-level error rate of previous approaches from 25.7% to 7.0%. (C) 2019 Elsevier B.V. All rights reserved.First author thanks the support from the Spanish Ministry "HISPAMUS" project (TIN2017-86576-R), partially funded by the EU. The other authors were supported by the European Union's H2020 grant "Recognition and Enrichment of Archival Documents" (Ref. 674943), by the BBVA Foundacion through the 2017-2018 and 2018-2019 Digital Humanities research grants "Carabela" and "HistWeather - Dos Siglos de Datos Cilmaticos", and by EU JPICH project "HOME - History Of Medieval Europe"(Spanish PEICTI Ref. PCI2018-093122).Calvo-Zaragoza, J.; Toselli, AH.; Vidal, E. (2019). Handwritten Music Recognition for Mensural notation with convolutional recurrent neural networks. Pattern Recognition Letters. 128:115-121. https://doi.org/10.1016/j.patrec.2019.08.021S11512112

Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification

This paper deals with the identification of Multiword Expressions (MWEs) in Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the Eight Schedule of Indian Constitution. MWE plays an important role in the applications of Natural Language Processing(NLP) like Machine Translation, Part of Speech tagging, Information Retrieval, Question Answering etc. Feature selection is an important factor in the recognition of Manipuri MWEs using Conditional Random Field (CRF). The disadvantage of manual selection and choosing of the appropriate features for running CRF motivates us to think of Genetic Algorithm (GA). Using GA we are able to find the optimal features to run the CRF. We have tried with fifty generations in feature selection along with three fold cross validation as fitness function. This model demonstrated the Recall (R) of 64.08%, Precision (P) of 86.84% and F-measure (F) of 73.74%, showing an improvement over the CRF based Manipuri MWE identification without GA application.Comment: 14 pages, 6 figures, see http://airccse.org/journal/jcsit/1011csit05.pd

Introductory Calculus: Through the Lenses of Covariation and Approximation

Over the course of a year, I investigated reformative approaches to the teaching of calculus. My research revealed the substantial findings of two educators, Michael Oehrtman and Pat Thompson, and inspired me to design a course based upon two key ideas, covariation and approximation metaphors. Over a period of six weeks, I taught a course tailored around these ideas and documented student responses to both classroom activities and quizzes. Responses were organized intonarratives, covariation, rates of change, limits, and delta notation. Covariation with respect to rates of change was found to be incredibly complex, and students would often see it as a series of steps rather than a simultaneous occurrence. With regards to rates of change, students went from seeing the average rate of change as some mean of variation to a change in y divided by the change in x within some acceptable error bound. Limits were a new concept to students, and they ended the course with an understanding of limits as finding an approximation for some value within an acceptable bound. Similar to limits, delta notation was also new to the students. Although it helped students better articulate their thoughts, the context in which students used it to describe change was oftentimes not mathematically rigorous. Besides these four narratives, evidence was also shown that students may gain deeper insights from problems based outside of the traditional physics context, such as velocity. These findings resulted in a list of suggestions of how the course might be implemented in the future so as to better ensure that students have a deeper conceptual understanding of derivatives.https://scholarworks.umt.edu/grad_portfolios/1278/thumbnail.jp

The Galois action on geometric lattices and the mod-ℓ\ell I/OM

This paper studies the Galois action on a special lattice of geometric origin, which is related to mod-$\ell$ abelian-by-central quotients of geometric fundamental groups of varieties. As a consequence, we formulate and prove the mod-$\ell$ abelian-by-central variant/strengthening of a conjecture due to Ihara/Oda-Matsumoto.Comment: Final version. Minor changes/corrections, introduction expanded. Will appear in Inventione

On the Expressive Power of Multiple Heads in CHR

Constraint Handling Rules (CHR) is a committed-choice declarative language which has been originally designed for writing constraint solvers and which is nowadays a general purpose language. CHR programs consist of multi-headed guarded rules which allow to rewrite constraints into simpler ones until a solved form is reached. Many empirical evidences suggest that multiple heads augment the expressive power of the language, however no formal result in this direction has been proved, so far. In the first part of this paper we analyze the Turing completeness of CHR with respect to the underneath constraint theory. We prove that if the constraint theory is powerful enough then restricting to single head rules does not affect the Turing completeness of the language. On the other hand, differently from the case of the multi-headed language, the single head CHR language is not Turing powerful when the underlying signature (for the constraint theory) does not contain function symbols. In the second part we prove that, no matter which constraint theory is considered, under some reasonable assumptions it is not possible to encode the CHR language (with multi-headed rules) into a single headed language while preserving the semantics of the programs. We also show that, under some stronger assumptions, considering an increasing number of atoms in the head of a rule augments the expressive power of the language. These results provide a formal proof for the claim that multiple heads augment the expressive power of the CHR language.Comment: v.6 Minor changes, new formulation of definitions, changed some details in the proof

Scope Management of Non-Functional Requirements

In order to meet commitments in software projects, a realistic assessment must be made of project scope. Such an assessment relies on the availability of knowledge on the user-defined project requirements and their effort estimates and priorities, as well as their risk. This knowledge enables analysts, managers and software engineers to identify the most significant requirements from the list of requirements initially defined by the user. In practice, this scope assessment is applied to the Functional Requirements (FRs) provided by users who are unaware of, or ignore, the Non-Functional Requirements (NFRs). This paper presents ongoing research which aims at managing NFRs during the software development process. Establishing the relative priority of each NFR, and obtaining a rough estimate of the effort and risk associated with it, is integral to the software development process and to resource management. Our work extends the taxonomy of the NFR framework by integrating the concept of the "hardgoal". A functional size measure of NFRs is applied to facilitate the effort estimation process. The functional size measurement method we have chosen is COSMICFFP, which is theoretically sound and the de facto standard in the software industry