Linguistic inversion and numerical estimation

Number line estimation (NLE) performance is usually believed to depend on the magnitudes of presented numerals, rather than on the particular digits instantiating those magnitudes. Recent research, however, shows that NLE placements differ considerably for target numerals with nearly identical magnitudes, but instantiated with different leftmost digits. Here we investigate whether this left digit effect may be due, in part, to the ordering of digits in number words. In English, the leftmost digit of an Arabic numeral is spoken first (“forty-one”), but Dutch number words are characterized by the inversion property: the rightmost digit of a two-digit number word is spoken first (“eenenveertig” – one and forty in Dutch). Participants (N = 40 Dutch-English bilinguals and N = 20 English-speaking monolinguals) completed a standard 0-100 NLE task. Target numerals were read aloud by an experimenter in either English or Dutch. Preregistered analyses revealed a strong left digit effect in monolingual English speakers’ estimates: e.g., 41 was placed more than two units to the right of 39. No left digit effect was observed among Dutch-English bilingual participants tested in either language. These findings are consistent with the idea that the order in which digits are spoken might influence multi-digit number processing, and suggests linguistic influences on numerical estimation performance.

Linguistic Inversion and Numerical Estimation

Number line estimation (NLE) performance is usually believed to depend on the magnitudes of presented numerals, rather than on the particular digits instantiating those magnitudes. Recent research, however, shows that NLE placements differ considerably for target numerals with nearly identical magnitudes, but instantiated with different leftmost digits (Lai, Zax, & Barth, 2018). Here we investigate whether this left digit effect may be due, in part, to the ordering of digits in number words. In English, the leftmost digit of an Arabic numeral is spoken first (“forty-one”), but Dutch number words are characterized by the inversion property: the rightmost digit of a two-digit number word is spoken first (“eenenveertig” - one and forty in Dutch). Participants (N = 40 Dutch-English bilinguals and N = 20 English-speaking monolinguals) completed a standard 0-100 NLE task. Target numerals were read aloud by an experimenter in either English or Dutch. Preregistered analyses revealed a strong left digit effect in monolingual English speakers’ estimates: e.g., 41 was placed more than two units to the right of 39. No left digit effect was observed among Dutch-English bilingual participants tested in either language. These findings are consistent with the idea that the order in which digits are spoken might influence multi-digit number processing, and suggests linguistic influences on numerical estimation performance.

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.

Arabic Handwritten Digit Recognition using Convolutional Neural Network

Arabic is the most widely used language in the world, especially the Arab League Country. Of course, in those countries often use Arabic numeral in banks and business applications, postal zip code and data entry application. This research has focused on handwriting recognition of Arabic numeral that has unlimited variation in human handwriting such as style and shape. The proposed method on the deep learning technique is Convolutional Neural Network. LeNet-5 architect also used in training and recognizing the handwritten image of Arabic numeral as much as 70000 images derived from MADbase dataset. The experimental result on 10000 images of database used is by comparing the number of epoch in training process yields, and the average accuracy is 97.67%.

No Gender Polarity in Arabic Numeral Phrases

Gender polarity is an intriguing morphological phenomenon in Arabic. The numerals 3–10 take the gender opposite to that of their count nouns; that is, when the count noun is feminine, the numerals 3–10 appear in the masculine form, and vice versa. Earlier analyses (see, e.g., Alqassas 2013, 2017, Alqarni 2015) proposed that the numerals 3–10 bear an inherent feminine feature, which is deleted by an impoverishment rule in the presence of a feminine feature on the count noun, yielding gender polarity. This article provides empirical counterevidence to these analyses and the concept of gender polarity on the whole. It shows that the numerals 3–10 do not interact with the gender of the count noun; rather, they interact with the count noun’s morphology—that is, whether the count noun bears the morpheme /at/ or /a:tu/-/a:ti/ in its structure. These findings suggest that gender polarity in Arabic is a misnomer; the phenomenon should instead be termed morpheme polarity. Rather than implementing the impoverishment rules proposed in earlier analyses, this article uses readjustment rules to account for the morpheme polarity at hand.

The syntax of (complex) numerals in Arabic

Word order, case assignment, and agreement for gender and number are realised with remarkable complexity in the Arabic numeral system. This paper examines the internal morphological structure of simplex, compound, and complex numerals. We identify a recurrent pattern found both inside complex numerals and in the structural relations between numeral and the nouns they quantify. The structures uncovered then allow for more principled accounts of the superficial morphosyntactic complexities. The analysis suggests that DP contains a single Num head, but that Num can express both additive and multiplicative arithmetic operations.

Roundtable Discussion III: The Development and Uses of Artificial Intelligence in Medicine: A Work in Progress

Humans have devised machines to replace computation by individuals since ancient times: The abacus predated the written Hindu–Arabic numeral system by centuries. We owe a quantum leap in the development of machines to help problem solve to the British mathematician Charles Babbage who built what he called the Difference Engine in the mid-19th century. But the Turing formula created in 1936 is the foundation for the modern computer; it produced printed symbols on paper tape that listed a series of logical instructions. Three decades later, Olivetti manufactured the first mass-marketed desktop computer (1964), and by 1981, IBM had developed the first personal computer. Computing machines have become more and more powerful, culminating recently in Google’s claim that it had achieved quantum supremacy in developing a system that can complete a task in 200 seconds that it would take the most powerful type of classical computer available 10 000 years to achieve. In short, we are in a period of human history in which we are creating more and more powerful and complex machines potentially capable of duplicating human intelligence and indeed surpassing/expanding its power. We are solidly in the age of artificial intelligence (AI). Increasing interest in the development of AI and its application to human health at all levels makes a roundtable discussion by experts a valuable project for publication in our journal, Gender and the Genome, the official journal of the Foundation for Gender-Specific Medicine and the International Society of Gender Medicine.

Arabic Numerals and the Problem of Mathematical Sophistication

This chapter begins with a critique of views on which the reference of a complex Arabic numeral is fixed by a sophisticated mathematical description. The chapter proposes instead that that reference is fixed by a convention that assigns to the numeral “1” the number one and that assigns to the numeral for n+1 the number greater by one than the number assigned to the numeral for n. This view, which presupposes that the sequence of Arabic numerals is learned independently of any principle of referential interpretation, evades the objections to sophisticated descriptivist theories. Toward the end of the chapter, a view of the referents of the numerals is defended according to which these are the finite cardinality properties. This view is argued to be the only one compatible with a number of principles constituting the intuitive conception of number and presupposed in the referential intentions of users of the numerals.

Handwritten Arabic Numeral Character Recognition Using Multi Kernel Support Vector Machine

Handwritten recognition is how computer can identify a handwritten character or letter from a document, an image or another source. Recently, many devices provide a feature using handwritten as an input such as laptops, smartphones, and others, affecting handwritten recognition abilities become important thing. As the mother tongue of Muslims, and the only language used in holy book Al Qur’an, therefore recognizing in arabic character is a challenging task. The outcome of that recognizing system has to be quite accurate, the results of the process will impact on the entire process of understanding the Qur’an lesson. Basically handwritten recognition problem is part of classification problem and one of the best algorithm to solve it is Support Vector Machine (SVM). By finding a best separate line and two other support lines between input space data in process of training, SVM can provide the better result than other classify algorithm. Although SVM can solve the classify problem well, SVM must be modified with kernel learning method to be able to classify nonlinear data. However, determining the best kernel for every classification problem is quite difficult. Therefore, some technique have been developed, one of them is Multi Kernel Learning (MKL). This technique works by combining some kernel function to be one kernel with an equation. This framework built an application to recognize handwritten arabic numeral character using SVM algorithm that modified with Kernel Learning Method. The result shows that the application can recognize data well with average value of Accuracy is 84,37%