R Meyer
email: remi.meyer@femto-st.fr

J Safioui

R Giust

P.-A Lacourt

L Furfaro

J M Dudley

F Courvoisier

Stealth dicing with ultrafast Bessel beams with engineered transverse profiles

We investigate high-speed glass cleaving with ultrafast laser beams with engineered transverse intensity profile. We achieve accuracy of ~ 1 µm at 25 mm/s and drastically enhance cleavability compared to standard Bessel beams.

Introduction

Ultrashort non-diffractive solutions such as Bessel beams have shown to be excellent tools for transparent material processing and cutting since they do not distort along the propagation and as they are robust to non-linear effects [START_REF] Bhuyan | High aspect ratio nanochannel machining using single shot femtosecond Bessel beams[END_REF]. In addition they allow for high intensity deposition control over long distance [START_REF] Courvoisier | Applications of femtosecond Bessel beams to laser ablation[END_REF]. This offers important opportunities for transparent material stealth dicing which is based on a two steps method [START_REF] Ahmed | Near-field modification of femtosecond laser beam to enhance single-shot pulse filamentation in glass medium[END_REF]: first a single pass laser illumination induces defects that weaken a vertical plane in the medium to be cut and second, material separation occurs after slight mechanical bending (i.e. cleaving).

Here we highlight the possibility to control the energy deposition in volume even better than with conventional Bessel beams, allowing for high speed material separation. We engineer a multiple transverse intensity distribution that preserves non-diffractive properties and allow for enhanced cleaving ability of glass. We compare sample mechanical response after laser illumination for different beam shapes and energies, using a 3-point bending test and modeling data with Weibull cumulative distributions. Cleavability and edge toughness are measured and compared, showing that transverse intensity distribution strongly impacts stealth dicing properties. We finally demonstrate glass cleaving for laser writing speed as high as 25mm/s and with accuracy approaching 1 µm over the whole 20 mm sample length and 150 µm thickness.

Experimental setup

We use a 800nm Ti:Sa source which delivers pulses stretched to ~2.3 ps duration full-width at half maximum (FWHM). We generate a primary Bessel beam that we demagnify with a telescope in order to increase the focusing angle to 17° over a 120µm FWHM distance in air. We process the samples 2 configurations: longitudinal and orthogonal as shown in Fig 1 .d and compare to Bessel beam processed samples. To quantify mechanical response after single pass laser illumination we bend the samples using a 3-points bending test (Fig 1 .e andf) and measure the required deflection to fracture, from the first contact to sample failure. We distinguish nice "cleaving" (i.e. separation in 2 pieces) from "breaking" which means a non-guided failure resulting several pieces from the initial sample.

Results and discussions

Figure 1 :

 1 Figure 1: 3-spots Modified Bessel Beams (MBB) generation proof of principle and mechanical investigations methods. We compare simulated and experimental normalized intensity distributions respectively in a) and b), while longitudinal profile is shown in c) exhibiting non-diffractive properties over 120µm. d) presents the different processing cases related to MBB and in comparison to Bessel beams. e) and f) compare mechanical investigations methods using a 3-points bending test respectively in cleaving toughness and edge toughness cases By spatial filtering in the Fourier plane of our telescope, we break the Bessel beam cylindrical symmetry and generate a novel beam shape (MBB) which features multiple hot spots (Fig 1.a and b) and non-diffractive properties

Figure 2 .

 2 Figure 2.a) shows the deflection needed to fracture the sample after single laser pass illumination for the different illumination schemes and energies. 6µJ processing does not provide sufficent defects for sample cleaving in all cases. For 12µJ and 18 µJ, we note a severe difference as longitudinal MBB provides mostly breaking while Bessel beams and orthogonal MBB systematically lead to nice cleaving (Fig 2b). In addition, we observe that for orthogonal MBB case, cleaving happens precisely along the laser processed path with an accuracy about 1µm over the whole sample length (Fig 2.b). Fracture propagates exactly at central channel middle that is induced by the most intense lobe of the MBB. Corresponding nanochannel is then open all over the edge thickness (Fig 2c). Additional measurements have shown the cleaved edge is even more robust after processing by a MBB than with a classical Bessel beam. Edge toughness improvement is about 30% using orthogonal MBB. Numerical simulations further confirmed the strong enhancement of stress generated in the sample by the orthogonal MBB (right of Fig 2.a).

Figure 2 :

 2 Figure 2: a) Mechanical deflection to fracture as a function of beam shape and energy. Comparison of Bessel beams on the left to MBB (resp. longitudinal case on middle; orthogonal case on the right). We distinguish cleaved samples (green disks) from broken samples (red stars). b) Optical microscope and c) Scanning Electron Microscope (tilt=52°) pictures of a sample edge, which was cleaved after illumination by orthogonal MBB.

Conclusion

We have demonstrated the feasibility of glass stealth dicing at extremely high precision with non-cylindrical diffractive-free beams. We have shown that stealth dicing of glass is controlled by the stress around laser-induced defects and not by the channels themselves and that transverse intensity profile strongly impacts the sample toughness. We believe this approach will foster new advances in ultrafast laser structuring of transparent materials. This research has received funding from the European Union Seventh Framework Programme [ICT-2013.3.2-Photonics] under grant agreement n°619177 TiSa TD


Yan-Feng Wang
email: yswang@bjtu.edu.cn

Ting-Ting Wang

Yue-Sheng Wang

Vincent Laude
email: vincent.laude@femto-st.fr

Manipulation of Acoustic Waves Based on Coupled-Resonator Acoustic Waveguides

Guidance in a sonic or phononic crystal is not limited to linear lines of defects. Actually, any chain of defects acting as cavities can become a waveguide, providing the cavities are not too far away from one another and they have the same initial resonant frequency. This is the basic idea of the coupled-resonator acoustic waveguide. Here, we first investigate dispersion relations of CRAWs based on a two-dimensional phononic crystal composed of cylindrical mercury in water in a square lattice. Cavity is introduced by replacing one mercury cylinder with water. Then, transmission properties of CRAWs with different circuits are examined. Numerical results suggest that manipulation of acoustic waves can be realized based on CRAWs. The work is this paper is relevant to the design of new acoustic wave devices.

Phononic crystals (PCs) are a kind of acoustic functional composite materials/structures with some forms of spatial periodicity 1 . They can exhibit elastic/acoustic bandgaps in some frequency ranges, within which the wave propagation is prohibited. One significant consequence of such a unique property is the possibility to control and manipulate the propagation of acoustic and elastic waves in integrated technological circuits. Consequently, numerous applications of PCs were proposed such as filtering, multiplexing, sound isolation, frequency dependent waveguiding 2,3 , and mass sensing.

Influence factors of band gaps and their modulation have become one of the research concerns. 'Banggap engineering' is thus proposed to realize the control of wave propagation. Mechanisms of band gap generation are mainly Bragg scattering, local resonance and their hybridization [START_REF] Laude | Phononic Crystals: Artificial Crystals for Sonic, Acoustic, and Elastic Waves[END_REF] . Frequency ranges of band gaps are dependent on the material parameters, lattice forms, topology of unit cells, and the connection form between scatters and matrix. For Bragg phononic crystals, the influential material parameters are the ratios of velocities and impendences between different components. The locally resonant band gaps, however, are generaly generated by the weak connection between scatters and matrix.

In addition to the analysis of band gaps, study of wave propagation in PCs also includes the confinement and guiding of elastic/acoustic energy through the use of defects in perfect PCs. Guidance in a sonic or phononic crystal is not limited to linear lines of defects 3 . A different guiding mechanism is coupling the evanescent fields of a linear chain of defect cavities or resonators inside the channel [START_REF] Sainidou | [END_REF]6 , similar to coupled resonator optical waveguides in photonic crystals 7 . In such waveguides, the dispersion relation is determined by coupling strength of the resonators allowing its fine tuning by varying their distance.

In this paper, we discuss the dispersion relations of coupled resonator acoustic waveguides (CRAWs) based on a two-dimensional PC composed of cylindrical mercury embedded in water in a square lattice. Defect cavity is introduced by replacing the mercury with water. Numerical simulation are implmented by finite element method (FEM). Cavities with different spearations are considered. Pressure distributions of eigenmodes at the band gap edges are also calculated. A theoretical model of linear chain of coupled resonators is also used to predict the CRAW dispersion relations. Then, we also investigate transmission properties of CRAWs with different circuits.

Numerical simulation is carried out by using the finite element software COMSOL. Numerical model of an isolated cavity is shown in Fig. 1(a). It is based on the two-dimensional PC composed of mercury cylinders embedded in water. The radius of the cylinder is r/a=0.45, with a being the lattice constant. Cavity is introduced by replacing mercury cylinder with water. The super cell contains of 7×7 unit cells, which is big enough to neglect the coupling between adjacent cavities. Periodic Bloch boundary conditions are applied on the opposite boundaries. For simplicity, only the localized waves are plotted in Fig. 2 by scatter-solid lines. The white part between the two gray ones represents for the bandgap of the original perfect PC.

When the separation between adjacent cavities,  , is not too large, as shown in Fig. 1(b) with 2a  , cavities can be coupled with each other by evanescen fields outside of them, creating a channel for guided waves. Band structures of the CRAW along x-direction is shown by scatter-dashed lines in Fig. 2. Guided waves are clearly found around resonance of a single cavity inside the bandgap: the dispersion curve bends gently. Analysis of the eigenmodes also suggests that these guided waves are generated owing to the excitation of the defect modes. And these CRAW dispersion can be predicted by using a theoretical model of a linear chain of coupled resoantors.

Then we also design CRAWs with different circuits in a finite structure. Transmission properties of these circuits are investigated, the shape of which is similar to the channeled spectrum in optics. Pressure distributions of some transmission modes are shown to under the physical meanchism of transmission peaks and wave guiding. Novel acoustic wave devices, such as a wave splitter, are also designed and demonstrated numerically.

The work in this paper can be extended for the control of elastic wave propagation by introducing fluid cavity in a solid-matrix PC 8 . Reconfigurable PC circuits are expected based on the coupled acousto-elastic resonators.

Figure 1

 1 Figure 1 Supercells used for the calculation of the band structures for (a) a cavity formed by a single point defect in the two-dimensional square lattice PC and (b) a coupledre sonator acoustic waveguide (CRAW) created in the same PC. The gray and blue parts represent for water, and mercury, respectively. The lattice constant between adjacent cylinders is a.

Financial

  supports by Fundamental Research Funds for the Central Universities (2016RC042) and Labex ACTION program (Contract No. ANR-11-LABX-0001-01) are gratefully acknowledged.

Figure 2

 2 Figure 2 CRAW dispersion for the cavity separation with 2a  illustrated by scatter-dashed lines. The scatter-solid lines are resonant frequencies for the isolated cavity. The gray area indicates the passing bands for the original perfect PC.


Ahmad Hawalah
email: ahawalah@taibahu.edu.sa

A FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS USING MACHINE LEARNING CLASSIFIERS

Keywords: Arabic, Sentiment Analysis, NLP

whether they are published or not. The documents may come

INTRODUCTION

Over the past decade or so, Internet users have been increasingly contributing to discussions on different topics such as sports, politics, brands etc, by adding their own comments and opinions into web pages and social media. Web 2.0 allows Internet surfers to share their views via social media such as Facebook, Twitter, Instagram etc. This has resulted in massive amounts of unstructured big data. Automated sentiment analysis is required to extract valuable information from the data [2] [3] [START_REF] Dashtipour | Multilingual sentiment analysis: state of the art and independent comparison of techniques[END_REF] [START_REF] Imane | Arabizi sentiment analysis based on transliteration and automatic corpus annotation[END_REF] [28] [START_REF] Ieracitano | A convolutional neural network approach for classification of dementia stages based on 2d-spectral representation of eeg recordings[END_REF].

The aim of sentiment analysis is to classify the data based on its polarity and automatically extract users opinion relating to relevant issues, events and services. User views are ex-pressed in a variety of ways, such as articles, reviews, forums, posts, comments, and tweets etc. The automatic extracting of these opinions is a very challenging task [1] [9] [START_REF] Dashtipour | Adaptation of sentiment analysis techniques to persian language[END_REF] [START_REF] Hawalah | Utilizing contextual ontological user profiles for personalized recommendations[END_REF].

There are currently two main approaches for sentiment classification such as machine learning and lexicon-based approach. The machine learning approach consists of supervised, unsupervised and semi-supervised techniques. The supervised technique contains a set of labeled data along with their class such as positive and negative. Based on these labeled data, machine learning classifiers are trained. The semi-supervised approach consists of labeled and unlabeled data. The machine learning classifiers are trained using the labeled data and predict the labeled for unlabeled data. The unsupervised technique is based on unlabeled data. The lexicon-based approach uses lexicons to identify the polarity of the sentence [4] [START_REF] Dashtipour | A comparative study of persian sentiment analysis based on different feature combinations[END_REF] [START_REF] Dashtipour | Exploiting deep learning for persian sentiment analysis[END_REF] [24] [START_REF] Gogate | Dnn driven speaker independent audio-visual mask estimation for speech separation[END_REF].

Arabic is the official language of 22 countries, spoken by around 400 million people. It is considered as one of the fastest growing languages in the world. There are about 65 million Arabicspeaking users online, which make up of around 18.8% of the global Internet population. Arabic is a Semitic language and consists of different dialects. These dialects are used in everyday communication and are not taught in schools. There is one formal standard writing in Arabic, termed as Modern Standard Arabic (MSA). There is a large difference between MSA and most Arabic dialects. Further, MSA is not a native language of any Arabic country, and is syntactically, morphologically and phonologically based on Classic Arabic [START_REF] Intisar O Hussien | Comparison of sentiment analysis approaches using modern arabic and sudanese dialect[END_REF].

The automatic identification polarity of Arabic text is difficult due to various factors. First, the 4479 complexity of the language with regards to both morphology and structure creates lots of problems and has resulted in limited tools and resources for the aim of sentiment analysis. Second, Arabic consists of different dialects and every region has its own dialect. Arabic is one of the top 10 languages used on the Internet, with various websites and weblogs specialized in Arabic reviews. However, no valuable research is available in Arabic [5] [52].

Sentiment analysis on social media has been the interest of a growing number of researchers worldwide. Many have used machine learning approaches to classify sentiments of English tweets. However, there is a less work carried out on the sentiment analysis of tweets for other languages [6] [START_REF] Ortigosa-Hernandez | Approaching senti-ment analysis by using semi-supervised learning of multi-dimensional classifiers[END_REF].

In this paper, we propose a new framework to extract Arabic features such as N-grams, from Arabic tweets datasets. Machine learning classifiers are used to comparatively evaluate the performance of selected features for Arabic tweets datasets. In the literature, extensive research has been carried out to determine the optimum features in English and Chinese languages in particular. However, to the best of our knowledge, Arabic feature selection for sentiment analysis is not yet well researched. This paper is organized as follows: Section 2 outlines related work in Arabic and other languages. Section 3, presents the proposed framework. Section 4 describes the comparative experimental results. Section 5 provides the discussion of the experiments. Finally, section 6 concludes the work and outlines some future work directions.

RELATED WORK

In the literature, extensive research has been carried out to model novel sentiment analysis for English, Arabic and other languages.

Arabic language

Shoukry et al. [51] have proposed an approach to detect polarity in Arabic tweets. These tweets are collected, pre-processed and converted into vectors. Finally, machine learning classifiers such as SVM and Nave Bayes are used to evaluate the performance of the approach. The experimental results show that SVM classifier (72.1%) has achieved better performance as compared to Nave Bayes classifier (65.4%). Abdulla et al. [7] have proposed an approach to identify polarity in Arabic reviews. In this approach large datasets are collected and manually annotated. Afterwards, two different classifiers are used to evaluate the performance of the approach. The experimental results show the SVM classifier (64.1%), which outperforms Naive Bayes classifier (55.9%). Ibrahim et al. [START_REF] Hossam S Ibrahim | Sentiment analysis for modern standard arabic and colloquial[END_REF] have proposed a feature-based approach to detect polarity in Arabic product reviews. The proposed approach introduces Arabic lexicon to detect idioms in Arabic language. Further-more, they develop lexicon to automatically detect polarity in Arabic reviews. Experimental results show the SVM classifier (95.12%), which outperforms Naive Bayes classifier (83.64%).

Al-Moslmi et al. [START_REF] Al-Moslmi | Arabic senti-lexicon: Constructing publicly available language resources for arabic sentiment analysis[END_REF] have introduced an Arabic sentilexicon for generating feature vectors and developed multi-domain Arabic corpus. They have evaluated the performance of the approach by using four different machine learning classifier. Experimental results show that SVM classifier (69%) outperforms Nave Bayes (53.87%), KNN (41.87%), and Logistic regression classifiers (45.87%). Al-Smadi et al. [START_REF] Al-Smadi | Using long short-term memory deep neural networks for aspect-based sentiment analysis of arabic reviews[END_REF] have proposed an approach to detect polarity in Arabic hotel reviews. The long short-term memory (LSTM) and neural network are used to evaluate the performance of the approach. The hotel reviews are converted into character embedding. Experimental results have showed the proposed approach (82.7%) outperforms to CNN (76.4%). Ahmad et al. [START_REF] Ahmad | Multi-lingual sentiment analysis of financial news streams[END_REF] have proposed a framework to detect polarity in the financial news in Arabic and Chinese. In order to evaluate the performance of the framework, the corpus is built, which consists of 8000 financial headline news. Finally, the data has been analyzed using grid-based analysis. The main disadvantage of the proposed framework is lack of machine learning classification.

Various languages

Sharma et al. [50]

have proposed an approach for visualization of Twitter messages. Online users are able to measure the sentiment. The main motivation is to build this application by providing an automated platform, which serves as complete endto-end system for sentiment analysis of Twitter messages along with their visualization. Saif et al.

[49] have developed a lexicon to detect polarity for sentiment analysis on Twitter. The proposed approach detects sentiment in entity-level and tweet-level. It has been evaluated using three different Twit-ter datasets. Experimental results show the proposed lexicon (81.03%) outperforms as compared to MPQA (63.79%) and SentiStrength (62.07%). However, the proposed lexicon only 4480 covers English tweets, and its not able to detect polarity in different languages such as Arabic.

Dashtipour et al. [START_REF] Dashtipour | Persent: a freely available Persian sentiment lexicon[END_REF] have proposed a lexicon to detect polarity in Persian sentences. The lexicon consists of 1500 words along with their parts-ofspeech tags and their polarity. The Persian headline news are used to evaluate the performance of the approach. The experimental results show the SVM classifier (67.03%) outperforms Naive Bayes classifier (61.2%). Grbner et al. [START_REF] Grabner | Classification of customer reviews based on sentiment analysis[END_REF] have proposed an approach to detect polarity in English customer reviews. The approach consists of three steps such as (1) building a lexicon, (2) apply lexicon to customer reviews and (3) apply machine learning to evaluate the performance of the approach. Experimental result shows the proposed approach has achieved accuracy of 90%.

Li et al. [START_REF] Li | Twitter sentiment analysis of new ikea stores using machine learning[END_REF] have proposed an approach to detect IKEA post. Similarly, they have developed a lexicon to detect polarity of the IKEA posts and evaluated it by using machine learning classifiers such as Logistic regression, SVM, Random Forest and neural network, Nave Bayes and elastic Net. The experimental results show the elastic Net classifier (80.04%) out-performs to logistic regression (70.35%), Nave Bayes (70%), SVM (70.65%), neural network (70.65%), and random forest (70.51%) classifiers. Wu et al. [55] have proposed a framework to predict emoji in English tweets. They have used a residual CNN-LSTM to evaluate the performance of the approach. The English tweets are converted to word embedding. This embedding is then fed into deep learning classifiers, which has resulted in an accuracy of 82.9%.

Basile et al. [START_REF] Valerio Basile | Sentiment analysis on italian tweets[END_REF] have proposed an approach to detect polarity in Italian tweets, and they have developed the first Italian tweet corpus. In order to collect Italian corpus, they have used language detection to collect only Italian tweets. The SVM has used to evaluate the performance of the approach which has resulted an accuracy of 66.4%. Kaya et al. [START_REF] Kaya | Sentiment analysis of turkish political news[END_REF] have proposed a framework to detect polarity in Turkish political news. The proposed technique has been evaluated by using four different machine learning algorithms such as Naive Bayes, Maximum entropy, SVM and character-based N-gram language model. Experimental results show SVM classifier (76.31%) outperforms Naive Bayes (72.05%), character N-gram (73.93%), and Maximum entropy classifiers (75.85%). Vural et al. [54] have presented a framework to detect polarity in Turkish text documents. The Turkish movie reviews are used to evaluate the performance of the approach, and the SentiStregth (English lexicon) is translated into Turkish to assign polarity into the dataset. Experimental results show the proposed framework has achieved an accuracy of 78.4%.

Ghorbel et al. [START_REF] Ghorbel | Sentiment analysis of french movie reviews[END_REF] have proposed an approach to detect polarity in French movie reviews. The French movie reviews are collected online, which consist of 1000 positive and 1000 negative labels. Unigram and parts-of-speech features such as adjective, adverb, verb and noun are extracted to evaluate the performance of the approach. Experimental results show the unigram has achieved accuracy of 93%. Kap et al. [START_REF] Kapociˇut¯e˙-Dzikiene | Sentiment analysis of lithuanian texts using traditional and deep learning approaches[END_REF] have proposed an approach to detect polarity Lithuanian text. The Internet comments in Lithuanian are collected, converted into word embedding using FastText, and classifiers such as Nave Bayes, SVM, CNN and LSTM are used to evaluate the performance of the approach. Experimental results show CNN classifier (87%) has achieved better accuracy as compared to SVM (61%), Nave Bayes (54%) and LSTM classifiers (73%). Rezaeinia et al. [START_REF] Mahdi Rezaeinia | Sentiment analysis based on improved pre-trained word embeddings[END_REF] have proposed an approach to detect polarity in movie reviews. These reviews are converted into word and parts-of-speech embedding. Afterwards, deep learning classifier such as CNN has been used to evaluate the performance of the approach which resulted an accuracy of 87%.

Wu et al.

[55] have proposed a semi-supervised approach using variational autoencoder to assign polarity to unlabeled datasets. The multi-domain datasets (car, book, laptop, news) are used to evaluate the performance of this approach. Experimental results show the proposed approach work effectively to detect polarity in unlabeled datasets.

However, none of studies have explored a sentiment analysis approach for general Arabic and Jordanian dialect datasets. Therefore, in this paper, we propose a novel approach to detect sentiment polarity in general Arabic and Jordanian dialects.

Table I summarizes some of the best approaches, which have been used for different languages and their reported accuracies.

METHODOLOGY

4481

In this section, the proposed approach for Arabic tweets sentiment analysis framework is discussed in detail. Fig 1 depicts the proposed framework and its details are presented in subsequent sections. The dataset is preprocessed using tokenization and normalization techniques. Afterwards, ngram features are extracted from the sentences, and finally machine learning classifiers are used to evaluate the performance of the approach.

Pre-processing:

The Arabic tweets dataset is used in order to evaluate the performance of the proposed framework. Dataset is distributed into training set (60%), testing set (30%), and validation set (10%) to apply machine learning classifiers. The dataset consists of positive and negative labels. The tweets are pre-processed using tokenization and normalization techniques. The NLTK tokenizer is used to tokenize Arabic tweets. For example, a text "Its a good mobile" is converted into words such as "Its", "a", "good", and "mobile". Afterwards, normalization techniques are used to normalize the tweets. For example, a tweet "It is gr8 time" is converted into "It is great time". [START_REF] Majumder | Sentiment and sarcasm classification with multitask learning[END_REF] [56] [START_REF] Khatua | A tale of two epidemics: Contextual word2vec for classifying twitter streams during outbreaks[END_REF].

N-gram: N-gram is a sequence of consecutive words in a text [START_REF] Amol | Twitter sentiment analysis approaches: A survey[END_REF] Ensemble Classifier Averaging: In this method, we take an average prediction of all the classifiers which is used to make the final prediction for accuracy, precision, recall and F-measure.

Ensemble(AVG) =   an a a a n      ..... 3 2 1 1 (1)

EXPERIMENTAL RESULTS

In order to evaluate the performance of our proposed approach, the tweets are converted into Bag Of Words (BOW). These BOW are fed into different machine learning algorithms such as SVM, Naive Bayes, Linear SVM, and RBF, to evaluate the performance of the approach. We

ASTD:

The Arabic tweets dataset consists of 1000 positive and 1000 negative tweets. These tweets are manually annotated into positive and negative labels by applying the Mechanical Turk approach. [START_REF] Nabil | Astd: Arabic sentiment tweets dataset[END_REF].

ATA:

The dataset consists of 1000 positive and 1000 negative tweets, which are of various topics such as politics and arts. These tweets are in MSA and Jordanian dialects [6].

Results:

The results for ASTD dataset are summarized into Table II and Table III. Among ngram features, classifier trained on unigram has achieved better performance in terms of accuracy.

The experimental results show the effectiveness of the unigram feature using MLP classifier. In order to evaluate the performance of the approach, the following evaluation metrics are used to calculate precision, recall, f-measure and accuracy. Table VI reports the comparison of the proposed framework for Arabic sentiment analysis with four recently proposed framework in the literature. In order to compare the results, we have used their dataset to evaluate the performance of our approach. Nabil et al. [START_REF] Nabil | Astd: Arabic sentiment tweets dataset[END_REF] have extracted unigram from ASTD dataset. TF-IDF has been applied into unigram and SVM is used to evaluate the performance of their approach. Rabab'ah et al. [START_REF] Abdullateef M Rabab'ah | Evaluating sentistrength for arabic sentiment analysis[END_REF] have developed a lexicon and ASTD dataset is used to evaluate the performance of the proposed lexicon. Al-Thubaity et al. [START_REF] Al-Thubaity | A saudi dialect twitter corpus for sentiment and emotion analysis[END_REF] have developed a lexicon for Saudi dialects, and they have used ASTD dataset to evaluate the performance of their lexicon. Table VII reports the comparison of proposed framework for Arabic sentiment analysis (ATA dataset) with recently proposed framework in the literature. In order to compare the results, we have used their dataset to evaluate the performance of our approach. Abooraig et al. [8] have developed a lexicon. Arabic tweets are used to evaluate the performance of the approach. Al Shboul et al. [START_REF] Bashar | Multi-way sentiment classification of arabic reviews[END_REF] have applied pre-processing technique known as stemming and used Naive Bayes classifier to evaluate the performance of the approach. Scikit learn (Python package) is used to train machine learning classifiers. The following experimental setup is used for our experiments:

DISCUSSION

The main finding is that the sentiment analysis of Arabic tweets is important and it can improve the accuracy for detection of polarity in short sentences such as tweets.

The main advantage of the proposed approach is useful to detect polarity in Arabic tweets. As mentioned earlier, Arabic language consists of different dialects such as Arabizi or Sudanese [START_REF] Intisar O Hussien | Comparison of sentiment analysis approaches using modern arabic and sudanese dialect[END_REF]. However, the proposed approach is able detect polarity in modern standard Arabic and Jordanian dialect. In addition, the proposed approach uses traditional machine learning classifiers such as MLP, SVM, and NB. However, deep learning classifiers such as Convolutional neural network (CNN) and Long short-term memory (LSTM) have not been used. The extraction of features such as unigram, bigram and trigram can be improved. Furthermore, feature engineering such as part-ofspeech tag features such as adjective, adverb, verb and noun can be used in the proposed approach. Finally, we have only evaluated the proposed framework for Arabic tweets. In the future, product and movie reviews in Arabic Language can also be used for comparative evaluation.

CONCLUSION

In this paper, a sentiment analysis framework has been proposed for Arabic twitter analysis. We have presented a comparative study analysis based on ngram features combination. The performance of the proposed framework has been evaluated by combining different features using two benchmark Arabic tweets datasets, and different machine learning classifiers. Comparative results show that the unigram features and MLP have achieved the best performance.

In future, we intend to apply deep learning classifiers to Arabic tweets and other benchmark datasets, and compare the results with other state of the art approaches..

  [START_REF] Ieracitano | Statistical analysis driven optimized deep learning system for intrusion detection[END_REF]. For example, , the unigram for sentence is . In the proposed framework, we have used unigram, bigram and trigram.

  have used different features such as unigram, bigram, trigram, mixture of unigram and bigram, mixture of bigram and trigram, and mixture of unigram and trigram to evaluate the performance of the dataset. Experimental results show the unigram achieved better accuracy as compared to other features such as Bigram, and Trigram. In addition, the Naive Bayes classifier has received lower accuracy as compared to other algorithms.

  T N + F P + F N where TP represents true positive, TN represents true negative, FP represents false positive, and FN represents false negative respectively. The unigram has performed better in comparison with bigram and trigram, because the bigram and trigram features contain lots of noise, which affects the performance of the classifier. Fig 2 summarizes 4482 the accuracy of the experiments for ASTD dataset and Fig 3 summarizes the accuracy of the experiments for Arabic tweets dataset.Table IV and Table V presents the Ensemble classifier and their results achieved on ASTD dataset and ATA dataset.

Table 1 :

 1 State-Of-The-Art Approaches And Achieved Accuracy

	Ref	Purpose	Language Accuracy
	Hassan et al. [29]	Develop Arabic lexicon	Arabic	74%
	Mostafa et al. [42]	Detecting polarity	Arabic	72.30%
	Donia et al. [21]	Detect polarity in Arabic tweets	Arabic	72.83%
	Zheng et al. [57]	Detect polarity in Chinese online	Chinese	74.42%
		reviews
	Liu et al. [40]	Detect polarity in customer reviews	Chinese	87.50%
	Jhanwar et al. [35]	Detect polarity in Facebook reviews	Hindi	70.80%
			English
	Van et al. [53]	Irony detection in English	English	65.90%
	Rauh et al. [47]	Detect polarity in German politic	German	72.30%
		comments
	Nakayama et al. [44]	Detect polarity in Japanese reviews	Japanese	76%
		Figure 1: Proposed Framework

Table 2 :

 2 results on astd dataset

	Table 3: result on ata dataset

Table 4 :

 4 Ensemble Classifier Averaging Results On Astd Dataset

	Feature	Precision	Recall	F-measure	Accuracy
	Unigram	0.7	0.71	0.62	71.74
	Bigram	0.65	0.68	0.56	68.38
	Trigram	0.59	0.68	0.56	68.21
	Unigram + Bigram	0.73	0.69	0.63	70.73
	Unigram + Trigram	0.73	0.67	0.62	70.49
	Bigram + Trigram	0.69	0.65	0.56	68.2

Table 5 :

 5 Ensemble Classifier Averaging Results On Ata Dataset

	Feature	Precision	Recall	F-measure	Accuracy
	Unigram	0.77	0.74	0.71	74.07
	Bigram	0.71	0.58	0.5	59.08
	Trigram	0.71	0.53	0.41	52.96
	Unigram + Bigram	0.65	0.68	0.61	67.54
	Unigram + Trigram	0.75	0.67	0.61	67.28
	Bigram + Trigram	0.71	0.57	0.48	57.59

Table 6 :

 6 Accuracy Performance And Comparison With State Of The Art Models -Astd Dataset

	References	Accuracy
	Our proposed approach (ASTD Dataset)	75.47%
	Nabil et al. [43]	68.07
	Rabab'ah et al. [46]	62%
	Al-Thubaity et al. [14]	65%

th September 2019. Vol.97. No 17 © 2005 -ongoing JATIT & LLS

REFERENCES:

[1] A dynamic partitioning algorithm for sip detection using a bottle-attachable imu sensor.


Introduction 1) The eyes as an access to the brain

Historically, orthoptics is the field that supported eye exercises as an instrument to recondition or improve eye fusion and teach the control of deviation. After formally becoming a practice in England in 1928, orthoptists worked closely with ophthalmologists, helping individuals, mainly children, to enhance their binocular vision (Helveston, 2005). Binocular vision refers to the overlap between the eyes which exists in all animals possessing both sight organs, and provides perceptive benefits such as resolution, detection, reading, discrimination and eye-hand coordination (Howard, 1995).

In the modern times, vision therapy prescribes eye exercises to combat a myriad of issues, to name a few: vergence, dyslexia, motion sickness, disorders of ocular motility, learning disabilities and amblyopia. While the seemingly abundant usage of these methods may look persuasive, only few studies have been conducted to determine the efficacy and assess the outcome of this kind of therapy. As a matter of fact, one study had found a measurable effect of these ocular trainings in patients suffering from convergence problems [START_REF] Noto | Eye exercises enhance accuracy and letter recognition, but not reaction time, in a modified rapid serial visual presentation task[END_REF].

Similarly, exercises of the eyes are also practiced in sports to boost performance and used in yoga to foster general well-being. The latter techniques seem to be related to not only states of deep relaxation but also used to lower arousal and promote wakefulness [START_REF] Hedstrom | Cognitive behavior therapy versus eye movement desensitization and reprocessing for post-traumatic disorderis it all in the homework then?[END_REF].

However, the control of the same vision organs is being currently employed in treatments of involuntary psychological conditions and has proved to be exceptionally efficient such as in eye movement desensitization and reprocessing (EMDR) for the treatment of Post-Traumatic Stress Disorder (PTSD) (Ho & Lee, 2012).

The current project discusses a technique known as the Quertant Method, which has been practiced for decades in France and has conveyed good results so far. Using eye exercises, this approach alleges to combat several disorders affecting the functional nervous system often manifested in neuro-vegetative, sensory-motor and even mental disorders. These conditions affect one's daily routines and cognitive performances as they are often comorbid and difficult to isolate, hence the difficulty to treat them medically.

While little has been scientifically reviewed and documented on its legitimacy, there is growing evidence on the relationships connecting the visual system to agents in charge of these disturbances. For instance, visual impairments have been observed in up to 90% of the cases with Cerebral Palsy (CP), a developmental disorder responsible for several motor disabilities in children. In an effort to inspect that, an experiment employed visual tasks with children affected by this condition to discover that control over their ocular movements was quite limited [START_REF] Gonzalez | Cerebral palsy characterization by estimating ocular motion[END_REF]. By the same token, plasticity mechanisms in the brain have been shown to occur in similar ways in both the visual and the somatosensory cortex [START_REF] Fox | A comparison of experience-dependent plasticity in the visual and somatosensory systems[END_REF]. Alternatively, because eye movements and visual attention are controlled by comparable regions in the brain, Noto and his coworkers used Rapid Serial Visual Presentation (RSVP) to find an enhanced effect of eye exercises on attention and memory tasks [START_REF] Noto | Eye exercises enhance accuracy and letter recognition, but not reaction time, in a modified rapid serial visual presentation task[END_REF]. Additionally, and in a recent study, Oldehinkel and her colleagues used functional Magnetic Resonance Imaging (fMRI). They showed a decreased connectivity between the networks of the somatosensory and visual association in 265 individuals with Autism Spectrum Disorder (ASD) [START_REF] Oldehinkel | Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor. Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project[END_REF].

It is estimated that the visual system treats between 80 and 90% of all incoming information from the environment, ranking for that reason, as one of the most demanding and energy consuming systems in the human brain [START_REF] Riley | Energy metabolism of the visual system[END_REF]. Correspondingly, sensory systems, are constantly being utilized by us, animals, to monitor the environment through the detection of pertinent features whilst filtering and deducting the irrelevant background information (Lampl & Katz, 2017).

Hence, the pile of the aforementioned scientific ingredients provokes the urge to question whether the entanglement of these systems can precipitate the vast array of disorders affecting our functional Nervous System.

2) The Quertant Method

The Quertant Method (QM) is a neuro-pedagogic method that works by training the eyes to presumably improve the functioning of unconscious and involuntary mechanisms that may constitute a precursor of various neurological and/or psychological disorders. This technique claims to target the central regulatory systems (CRS) of the central nervous system (CNS) and acts by reducing or eliminating their functional issues. In order to access the hub of our sensations, thoughts, emotions and actions that characterize the CNS, the Quertant Method focuses on coaching the functional visual system [START_REF] Marchesseau | Neuro-Pédagogie[END_REF] through which it supposes the activation of the auto-regulatory capacities of the CNS.

The neuro-pedagogy judges that oftentimes, these natural capacities get misaligned causing a desynchronization of the original self-regulatory systems in the base of the brain. Thus, leading to the emergence of multifarious symptoms or disorders whose origins might become difficult to set apart. These many and usually common disorders can be classified in the following three categories:

 Neuro-vegetative nervous system disorders; e.g., digestive problems;  Sensory-motor disorders; e.g., dyslexia and stuttering;  Mental disorders; e.g., anxiety and burnout.

Given the subtle nature of the Quertant Method and the scarcity of scientific background supporting its impact, I have dedicated the past few months to learning about its origins, its tangible effects on individuals who have practiced it, and attempted to find scientific links that may explain the operative aspects of this reported successful neuro-pedagogic method. While pursuing that, I was fortunate to meet with Dr. Catherine Larcher, who has been practicing the Quertant Method for decades now and presently still. With her, I learned and experienced myself an initial trial test that helped me get more in-touch with the psycho-sensory training mechanisms.

To further my knowledge and fulfill the global purpose of this research, I created a Survey comprising 40 questions, designed to collect the impressions of previous Quertant Method learners' and, through their feedback, measure the progression of their conditions. This questionnaire was answered by 23 individuals and targeted multiple facets of the Method. The results recorded will later be discussed in detail.

3) Overview of the Quertant Method

According to optic laws studied in a relationship with the method, the nervous centers responsible for eye movements direct a light beam to the fovea, the most sensitive area of the retina. However, dysfunction arises when the print axis of the eyes and the projection axis no longer overlap [START_REF] Marchesseau | Neuro-Pédagogie[END_REF]. In other words, the coordination of both eyes falls short on producing a single accurate mental impression, and as a result, the person sees a distorted image.

To practice the Quertant Method, a specific setting is required, mainly a room deprived of light, optic machines such as diploscopes; a tool used to educate the eyes while looking through six circular holes, at stimuli that are generally based on color, oftentimes primary ones, or printed letters, in black ink on a white surface. (Fig. 1) below shows pictures of the typical arrangement of participants looking through a diploscope practicing the Method with color and letter stimuli. The training commonly happens over two stages; First, an initial assessment is made to identify the participant's functional visual issues that are primarily related to binocular vision.

Unlike monocular vision, this type of vision is extremely crucial for depth perception (Howard, 1995). During this first evaluation, the person undergoes a series of tests for a period of time estimated at 1h30. Secondly, the visual reeducation begins depending on the participant's dysfunctional visual aspects determined at the first stage. On average, the sessions last for 30 minutes and are held twice a week, during an extended time stretching from months to even years.

Quertant Method is close in its principle to biofeedback, which is a technique or form of therapy which engages one to monitor their own physiological states in order to modify their functions (McKee, 2008). Neurofeedback, on the other hand, compels the recording of brain activity. It is also often referred to as "EEG" biofeedback [START_REF] Hammond | What Is Neurofeedback?[END_REF]. This procedure allows one to gain control over involuntary functions so as to modify those of the autonomic body such as one's emotional states. For instance, EEG attention waves (theta waves) are recorded and the subject is encouraged or reinforced each time their brain is showing higher levels of those waves. Accordingly, they learn to favor their attentional states. Neurofeedback however, acts on a restricted area corresponding, in this instance, to attention EEG waves, whereas the Quertant Method is supposed to reach a much larger spectrum. Although the sessions are prolonged, it remains an exercise that, after few first sessions of guidance, one can pursue it independently using the same tools and stimuli.

4) Background: Who was Georges Quertant?

As the name suggests it, this Method was named after its founder Georges Quertant, born in Digne, France in 1894. Georges grew up in Amiens where he befriended the famous storyteller Jules Verne. Later, he had a long and meaningful kinship with one of the most renown physiologists at the time: Claude Bernard. Claude may have been the most influential person whose workand friendshipinspired the profound physiological work that Georges embraced subsequently.

At the age of 16, the teenager Quertant was already a musician; composing and playing piano. Not only was he deeply involved in music but also curious about its effects on human beings. By taking advantage of his brother who, then, was a doctor in the "Amiens Hospital", Georges started frequenting the Psychiatric Departments of the building and carefully observing the influence of music on the patients' behavior.

Following the years, he conducted several clinical studies alongside his sibling and, from there, devoted almost 3 decades of his life to research, the understanding of development and the well-being of others. Thanks to his focused work, the admiration of scientists, doctors and educators around him multiplied to make him President of the "Scientific & Literary Society" in Cannes, France. Moreover, in 1952, he was decorated knight of "The Legion of Honor" & received the award of the "Order of Academic Palms distinguished Officer".

With dynamics such as his, George Quertant had an impulse to innovate and blend his music skills with his acquired research skills. Departing from that motivation, he developed the "Psycho-Sensory Culture" that became recognized by the French Ministry of Public Health in 1942. Today it is known as the "Quertant Method" or in the modern tongue as the "Neuro-Sensory

Training".

5) The rise of psycho-sensory culture

At the hospital with his brother, Georges studied the influence of sound, using sound pictures on human behavior. Due to the underdeveloped sound technics at the time, he reckoned that the results obtained from the sound stimuli could also be accomplished through light stimuli.

From thereafter, the relationship linking the quality of the functional nervous system with the quality of vision lit up his inquisitiveness. Meanwhile, he condemned the mono-directional attention that physiologists gave then to the ascendant pathways in the brain, traveling from the retina and ending at the cortex, and the negligence of the descendant ones which begin at the cortex and sway toward the retina [START_REF] Marchesseau | Neuro-Pédagogie[END_REF].

After expanded research, he suspected that several functional disorders originated from the dysregulation of the nervous system's regulating centers located at the base of the brain.

Considering the retina to be the extended window to it, he theorized that the hypothalamus in the brain was directly acted upon through visual stimuli. Today, the existence of a retinal projection to the hypothalamus is recognized after it has been revealed in several experimental studies with animals (Sadun, 2009).

By delving more into the human physiology, Georges started sketching the areas of the brain, distinguishing between those located at the upper part, ensuring the conscious and voluntary activities such as the cerebrum shown in (Fig. 2) as "télencéphale" in French. And those found at the base of the brain such as the mesencephalon or midbrain, and the diencephalon shown respectively as "mésencéphale" and "diencéphale" in (Fig. 2).

According to him, these adjoined areas of the brain containing the thalamus and the hypothalamus, control the involuntary functioning of the organism. (Fig. 3), represents a sketch made by the master of the Method delineating the auto-regulatory centers in the brain that he believes can be reached through the visual system. Given their complexity, a single perturbation affecting these foundational regulatory centers responsible for mechanisms outside of our consciousness, would ostensibly engender multiple repercussions on one's well-being. Here, the artist and scientist spirits of Georges Quertant are exhibited in the immaculate and fastidiously illustrated drawing with explanatory numbers along with their legend.

For instance, number (8) represents the "nervous organization of vision" propagating over (1) the thalamus, (2) the corpus striatum and (3) the hypothalamus, branching all the way to the medial occipital lobe where the visual cortex is located.

After his passing in 1964, his works devolved to his daughter Marguerite Quertant born in 1923, who had started following her father's steps as early as in 1939. She then, devoted her time and attention to practice and share the mechanisms underlying this deemed transformative technique and its advantages.

From Cannes where she was born and raised, Marguerite also practiced the Method at the Principality of Monaco. Gradually, she started training educators, and in 2008 she opened the "C.P.S Quertant" standing for the "Centre Psycho-Sensoriel Quertant" in French and translating into "Psycho-Sensory Quertant Center". To thank her for her keen dedication and perseverance, the city of Cannes later decorated Marguerite Quertant with the Golden Medal [START_REF] Marchesseau | Neuro-Pédagogie[END_REF].

Marguerite's efforts to further promote her precious heritage were reflected in her commitment to its dissemination nationally and internationally. She henceforward took trips to big cities around the world such as Rome, London, Los Angeles and Paris. In there, she presented it in conferences among which were consecrated to medical, mental health and psycho-somatic research. However, and despite her efforts, the Method remained quite confidential. The first assessment is generally envisioned for the practitioner to objectively measure the intensity and meaning of the dysfunction. The purpose being to either attenuate or remove it using the eye stimulation. A series of at least 22 tests are conducted, using 3 to 5 optical machines calibrated with the tests presented at different distances. These tests are based on either; colors (blue, red, green and yellow) alone, neutral letters alone, or colors and letters together.

Nonetheless, their distances from the trainee are adapted according to the symptoms or disorders affecting their functional nervous system.

The means by which the specific distances used in the diploscopes have been established are empirical. Similarly, the rationale for the use of the color vs. letter customization is unclear.

According to Georges Quertant, the letters would operate on the mental related disturbances such as anxiety, phobia and low self-confidence whereas the colors would be associated with one's affective aspects such as emotion regulation and motivation. Although it is now known that the brain uses different neural networks when interpreting colors vs black and white shapes, such as letters in the visual scene, the networks involved in higher level interactions remain elusive.

As far as the distances go, the Quertant Method maneuvers 3 different distances; 1m20, 60cm and 30cm, between the trainee and the stimuli they are facing. Using 1m20 presumably works on general tension and sensorimotricity. 60cm is supposed to target issues relevant to the neurovegetative or autonomic system such as digestive problems, palpitations, allergies and sleeping disorders. Third and last, are the mental and emotional states supposed to be aimed at from a 30cm distance.

The head is well aligned with the diploscope during the training. The stimuli consist of, for instance, the letter D, O, G and E, each embedded in a color background. Looking at this example of a Quertant test below, one would normally perceive the letters as shown in Fig. (a), where the circles are distinct, and the distances are precisely the same in between. However, according to the method, in the case of hypo-reactivity for example, the eyes converge the middle stimuli resulting in the blending of the colors and/or letters. On the other hand, in the case of hyper-reactivity of the nervous system, the colors and letters are perceived divergently.

I. Building a Survey

Methods

In order to evaluate the effectiveness of the Quertant Method, my project directors, Alice

Guyon and Bruno Cessac, in addition to myself thought that the creation of a survey would provide a measured assessment of the opinions and feelings of previous trainees. In this way, it would afford a better understanding of the parameters coming to play a reframing role on one's regulatory systems.

Thanks to Alice and Catherine's contributions, we were able to incorporate and agree on 40 pertinent questions on the survey that was programmed using Qualtrics. Then after, I provided an anonymous link to Catherine who diffused it to 50 of her previous trainees via e-mail or text message (SMS). In this way, participants were able to take it at their will on any of their electronical device (phone, tablets, laptops). For the participants' convenience, the survey was written in the French language. The questions assessed the experience of 23 individuals, 16 women and 7 men, who have previously practiced the Quertant Method with 1 person who is yet currently doing so. The text of the survey is given in Annex 1.

The ages of the volunteers ranged between 9 and 80 years old (mean= 51.04, std=50.2).

Most participants practiced the method for a minimum of 3 months and a maximum of 24 months (std= 53.74). Additionally, to assess multiple facets of the Quertant training, the questionnaire was organized in four main sections:

The first one listed 20 major symptoms/disorders that the Quertant Method at which it is supposed to be directed, such as attention disorders, depression, hyper-emotivity, etc. In the first instance, participants were asked the following question "Before the Quertant Method, have you suffered from one or more of the symptoms/disorders below?". They also had to select the conditions they were experiencing maladaptation in before the training. After that, they were asked "Have you felt any improvements on one or more of the following symptoms/disorders?" and had to select the ones where they experienced improvement.

Another section looked at the judgement of the Quertant trainees on the easiness vs.

complexity of each of the exercises used during the practice, the questions were formulated in this manner: "During your practice of the Method, which exercises seemed easiest/most difficult for you?". Based on those criteria, participants were asked to rate each stimulus, color and letter, at each of the employed distances (closer, intermediate and farther).

The third section of the survey asked whether the subjects perceived improvements on general traits such as emotion regulation, personality, vision and self-confidence. Volunteers who answered "yes" to experiencing progress were later asked to rate it for each of the traits by choosing one of the three options: "Much better" "Somewhat better" "Somewhat worse". Then, they were invited to describe in few words the observed enhancements in each.

Last but not least, the final section of the survey was dedicated to questions assessing the participants' levels of satisfaction with the training, the final outcome and the Method overall.

Coming along with a segment dedicated to additional comments and impressions they may desire to add. To give an example of the interrogations' wording; "Overall were you satisfied with the final outcome of the training?" and ". By the end of the training, did the Quertant Method help you achieve the goals you had set prior to starting it?"

II -Survey Results

Section 1

Chart (1) below, shows the comparative results of all the responses on the 20 conditions in addition to "Claustrophobia", which did not belong to the suggestive list yet was reported by one participant.

Results showed at least 50% improvement on all listed symptoms/disorders, meaning at least half of the participants who reported suffering from certain conditions also reported experiencing improvement on those same conditions.

On several disorders, 100% improvement was recorded, namely on: Depression, nightmares, distress, nervous fatigue, dizziness, allergies, memory disorders, hyper-emotivity, digestive disorders, memory disorders and claustrophobia. This result brings further evidence to the effectiveness of the Quertant Method since these symptoms fall onto the spectrum of the 3 main functional nervous system dysregulations that Georges Quertant claimed his Method to directly target. Moreover, more than 80% improvement was registered on migraines, stress and distress.

The most compelling evidence figures in the outcome of three other conditions; attention disorders, shyness, and obsessions, that have exceeded the 100%. To put it differently, participants who may have been unaware of the existence of disorders related to their attentional capacity, degree of timidity or obsessive behavior, have realized and declared improvement on them following the training.

Chart (1) l Reported symptoms or disorders prior and following the Quertant Method

Section 2

Overall exercises using color stimuli were found to be easier than letters. However, colors and letters at closer distances (30cm) were reported to be the most difficult, whereas the same stimuli were found to be the easiest at a farther distance (1m20). Towards the completion of the training, progress was witnessed on each of the visual exercises. More importantly, 12 individuals reported an amelioration of their visual performance on all. Chart (2) below illustrated the results obtained on all exercise ratings in addition to the evolution relayed by the survey takers. The numbers refer to the number of participants' rating each of the exercises based on the level of simplicity, complexity and later progress. It is visually noticeable on Chart (3) that several improvements were noted on seven personal attributes following the practice. According to these results, the three most improved ones are respectively; emotion regulation, self-confidence and personality. The number of participants attesting feeling a significant improvement (selecting "Much better") exceeds the number of participants reporting a marginal one (selecting "Somewhat better").

Considering that, in this section, a statement was included enabling participants to describe in their own words the changes felt, each trait will be discussed separately.

Emotion regulation:

With 15 individuals reporting great improvements on this trait, comments were revealing of influential impacts and some were even reoccurring among the trainees such as improved ability to step-back mentioned by 3 individuals. Other impressions involved better self-confidence and focus on one' self, stronger ability to let go, decreased impulsiveness, emotional distress and somatization. In addition to that, another impression involving fewer palpitations in extreme situations was also observed.

Self-confidence:

Thirteen individuals reported experiencing a significant improvement related to selfconfidence, and 4 additional ones rated it being moderate. Their additional comments involved the following: Enhanced capacity to say 'no' and to stand by one's opinions and to confront people.

Better levels of awareness were also mentioned along with increased serenity and firmness, stronger belief in one's capacities. Besides, one participant wrote "I finally feel myself", and another student observed "better classroom participation".

Personality:

About 65% of the survey takers reported experiencing improvements as far as their personalities go, 80% of which estimated these changes as significant. A couple of people reported accentuated feelings of overall positivity, better self-affirmation and confidence. Intensified feelings of calmness and serenity were also mentioned by several. Further shifts were reported such as reduced fearfulness and better grounding.

Vision:

15 individuals reported having better vision, with 8 of them feeling significant changes, 6 moderate and one person reporting a deterioration. Contrary to all other assessments, this was the only case reporting a decline in one of the attributes. This odd event may need to be looked further into to understand the nature of this negative alteration. On another note, the positive opinions were many and remarkably identical across participants. Six individuals pointed out ameliorations related to nearsightedness (distant vision), convergence and ability to perceive fine details.

Moreover, among them 3 trainees who declared an increase in their experienced visual field. And finally, 3 additional persons reported giving up glasses, mainly for reading.

Attentional capacities:

One of the main areas the Quertant Method claims having an impact on is one's attentional capacities. According to our survey's results, 10 individuals witnessed a great impact on their attention. Onepresumablystudent declared being more attentive in the classroom therefore expressing better retention of their course' material. Another Quertant trainee reported working long hours without having any feelings of stress or frustration. Furthermore, other trainees report having better concentration, reactivity and accuracy.

Cognitive performance:

More than half of the survey takers ( 12) reported progress on their general cognitive performances. Many expressed clearer thoughts and reflections, improved ability to generate faster ideas for finding solutions, better at calculus, and finer perceptions of the world and the environment.

Sleep -Quantity and Quality:

The lowest rate was recorded on this activity with only 9 individuals experiencing changes.

This could be explained by the fact that none of the survey takers reported having sleep disorders, therefore not expecting any changes. However, 6 individuals marked a great improvement on the quality of their sleep in comparison to only 4 regarding its quantity. Since the questions tried to assess each sleep aspect separately, no additional comments were given.

Section 4

In this section, one piece of data was omitted, belonging to the subject who had not finished their Quertant Method training yet, resulting in 22 responses instead of the original 23.

Participants' impressions were overall positive and encouraging. For example, the question asking "In general, were you satisfied with the final results?" 21 responses were recorded for "Yes" whereas only 1 person responded "Neutral". When asked about the pedagogy and the methods used, 12 trainees reported it being "Excellent", and 9 rated it being "Good" with one person who remained neutral. Another question was interested in knowing whether these volunteers achieved the objectives they had set to achieve in the beginning. Over 77% (17 individuals) answered "Yes" whereas the remaining 5 noted "partially".

Furthermore, the survey takers seem to be willing to recommend the Quertant Method to their closed ones, as 19 agreed to the statement suggesting whether they would be open to do so, and 3 individuals responded "maybe".

The final question was an open commentary arena where any additional impressions could be inserted. Many compliments were then registered on this section, all received comments on the training are reported on the table below with the exact responses collected in French (left side) and their translated English equivalence (right side).

Do you have any additional comments or impressions

Discussion

In light of these rich survey results, many conclusions can be drawn; first of all, it was demonstrated that the Quertant Method is a practice that satisfies, to a certain extent, its purpose and claims. Although it has not been scientifically tested yet, the testimonies of its loyal mentees bear witness to the revolutionary benefits it may bring about.

It can be argued that the volunteers' investments of time, energy and financial resources may have skewed their perception of the Method, therefore affecting their testimonies. However, it is hard to accept that the individuals who agreed to take this survey along with the thousands of people who have practiced it and certified its authenticity to have fallen prey to a placebo effect.

It is also worth noting that some volunteers noticed ameliorations on symptoms they were initially unaware of, thus illustrating an original and genuine point of view.

Alternatively, another factor that may have intervened in these survey results is the gender criterion, given that the number of women ( 16) largely exceeded the number of men ( 7 Finally, I would like to thank my project directors; Alice Guyon for giving me the opportunity to take part in this exciting project, whose prospects look quite promising, and Bruno Cessac for his input and continuous support. I would also like to thank Catherine Larcher for facilitating the research, the learning and the distribution of the survey.

Annex 1

Questionnaire : Méthode Quertant Nous vous remercions d'avoir pris le temps de répondre à nos questions.

Les informations partagées seront utilisées dans un cadre de recherche neuro-scientifique de la Méthode Quertant.

MERCI !

Fig. 1 .

 1 Fig. 1. l Participants practicing the Quertant Method

Fig. 3 l

 3 Fig. 3 l Georges Quertant portrait of the central autoregulatory systems or the somato-psychological complex

Fig. 2 l

 2 Fig. 2 l Georges Quertant depiction of the human encephalon

Fig

  Fig. (a) Test normally perceived

  Easy vs. difficult eye exercises and perceived progress. Perceived post-training improvements on 7 personal attributes.

  Difficile de répondre plus précisément... Le temps a passé et a changé les perceptions... Hard to answer precisely… Time has passed and my perceptions have changed Cette méthode est très particulière car durant les quelques premières séances je me suis dit que cela n'avait aucune incidence sur moi. Après 3 semaines â un mois de séances, je me suis sentie comme ayant repris le contrôle de moi-même. J'ai suivi cette méthode 2 fois à 10 ans d'intervalle. Elle m'a aidé les 2 fois à effacer cette claustrophobie qui m'empêchait de prendre l'ascenseur, l'avion, passer sous les tunnels… This is a unique Method, during the first sessions I thought that it had no impact on me. After 3 to 4 weeks of training, I felt as I had regained control over myself. I followed this Method twice over an interval of 10 years. It has helped in both instances to eradicate this claustrophobia that prevented me taking the elevator, the airplane or crossing under the tunnels… Très efficace là où les psychologues ne peuvent aller Very efficient, goes where psychologists cannot reach je crois en son efficacité surtout pour les enfants et les jeunes gens I believe in its efficacy especially with kids and younger people Méthode à faire connaître plus This Method needs to be further recognized Cela m'a permis de prendre conscience de ma valeur It has helped me to become conscious of my own worth la personne qui encadre est essentielle à une bonne évolution The person guiding the training is important for a good evolution Méthode longue mais efficace pour consolider des changements en profondeur Long Method however efficient to consolidate deep changes la faire reconnaître en tant que méthode médicale Needs to be recognized as a medical procedure Excellent outil thérapeutique Excellent therapeutic tool Qques séances de tps en tps qq tps après seraient peut être bien comme un suivi à mettre en place Organizing occasional follow-up sessions after the end of the training may be a addition J'avais 23 ans donc une bonne vue, d'où aucune amélioration à ce niveau I was 23 years old with good vision, therefore no improvement was perceived

  ) in responding to this questionnaire. It would be worth inspecting in the future, whether this practice has the same impacts on both genders. Likewise, only one child of 9 years old participated in our survey, making it an insufficient representation of the implications of the method during early years. Especially considering the fact that sensory processing disturbances have been found to arise early in development, around the first 3 years of the child's life. Today, with the advancements in research, these disturbances figure among the symptoms of various neurodevelopmental disorders and psychiatric illnesses, such as Autism Spectrum Disorder (ASD), Attention Deficit Hyper Activity Disorder (ADHD), and Obsessive Compulsive Disorder (OCD).ConclusionGeorges Quertant's precocious understanding of the human brain is visible through his meticulous, extremely detailed, and precise depictions of the Nervous systems, the specialized brain areas and the correlations between our senses and regulatory centers. Although recognized by the French Ministry of Public Health, it is true that little has been scientifically studied to affirm or refute Georges' work. However, evidence supporting his findings may further lie in the positive testimonies of the thousands of individuals who have benefited from his psycho-sensory culture around the rooster country.This project was initially designed to build a scientific model elucidating the functioning of this Method. For time-related constraints, a review of its background had imposed itself first, leading to the birth of this version. During my examination of the subject I had the pleasure to meet with Catherine Larcher, a psychologist who had trained with Marguerite Quertant and has been practicing the method over decades with both children and adults from both genders. With her, I was able to sit at the trainee's chair and experience the testing myself. Dr. Larcher did her doctorate thesis on this technique, and she testifies of the authentic and life-changing results she has witnessed throughout her experience over the years.A study looking at the effects of the Quertant Method on children with ADHD has been in the works and coming close to launching, presided by my mini-project Director Alice Guyon, a research director at the Pharmaco-Molecular and Cellular Institute in Nice, France, and who also recently finished her Quertant training. Thanks to her, I was introduced to this Method and came to learn more about its potential involvement in several disorders. The overwhelming evidence supporting this psycho-sensory training suggests greater prospects in the future. Further studies can use more advanced technological devices such as electroencephalography (EEG) that can be worn during the practice sessions to monitor electrical brain activity of the participants. Comparing by imaging the brain before and after the Quertant Method could also demonstrate the changes occurring during the training. Functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) could particularly bring to the spotlight the Quertant Method's mechanisms of action in the brain. Additionally, in the context of the modernization of the method, perhaps a virtual version can be programmed and tested via eyetracking to further knowledge about the precise movement of the eyes during the training. In conjunction with these measures, physiological states can also be supervised to determine whether any observable shifts are happening during the sessions.

questions suivantes visent à connaitre vos impressions sur la Méthode Quertant.

  Chère participante, cher participant, Ce questionnaire a été créé dans le cadre d'une recherche cherchant à établir les bases neuroscientifiques sous-tendant la Méthode Quertant, ainsi que son influence potentielle sur le système nerveux et la vie quotidienne. Veuillez noter qu'aucune réponse n'est fausse ou correcte. Nous sommes simplement intéressés par l'évaluation honnête et naturelle de votre expérience avec cette méthode psycho-sensorielle. C'est pourquoi nous vous remercions de prendre quelques minutes (< 10min) pour répondre à ce questionnaire.

	o Oui, j'accepte o Non, je n'accepte pas MERCI ! Quelles sont vos initiales? En quelle année êtes-vous né(e) ? Quel est votre sexe? o Femme o Homme o Oui o Non Pendant combien de temps avez-vous pratiqué la Méthode Quertant? Les Pratiquez-vous actuellement la Méthode Quertant? (

Acceptez-vous de répondre à nos questions concernant la Méthode Quertant?

Veuillez préciser le nombre de semaines, mois ou années)

  Selon vous, est-ce que votre capacité attentionnelle a changé après la pratique de la Méthode Quertant? Beaucoup mieux Pouvez-vous décrire en quelques mots le(s) changement(s) lié(s) à votre capacité attentionnelle ? Selon vous, est-ce que vos performances cognitives ont changé après la pratique de la Méthode Quertant ?Selon vous, est-ce que la qualité et/ou quantité de votre sommeil a changé après la pratique de la Méthode Quertant ?

	o Troubles du sommeil o Troubles de l'attention o Troubles digestifs o Palpitations Si oui, dans quel sens? o Oui Si oui, dans quel sens? Si oui, dans quel sens? o Moins bien o Non Moins bien o Je ne sais pas o Un peu mieux o Beaucoup mieux Qualité du sommeil o	o Timidité o Vertiges o Bégaiements o Angoisse Un peu mieux o	Beaucoup mieux o
	o Hyperémotivité Pouvez-vous décrire en quelques mots le(s) changement(s) lié(s) à votre personnalité ? o Dépression o Moins bien Quantité du sommeil o o	o
	o Troubles de la mémoire o Un peu mieux	o Obsessions
	Quand avez-vous cessé de pratiquer la Méthode Quertant ? Combien de séances en avez-vous fait jusqu'à ce jour? Avant la Méthode Quertant, souffriez-vous de l'un ou de plusieurs symptômes/troubles ci-dessous? (Veuillez cocher toutes les réponses qui s'appliquent) o Allergies o Anxiété o Stress o Dyslexie o Phobies o Cauchemar o Fatigue nerveuse o Migraine o Autre(s) ____ o Un peu mieux o Non Selon vous, est-ce que votre vision a changé après la pratique de la Méthode Quertant ? Selon vous, est-ce que votre gestion des émotions a changé après la pratique de la Méthode Quertant ? o Oui o Non o Je ne sais pas Si oui, dans quel sens? o Moins bien o o Oui Les
	o Troubles du sommeil o Oui o Beaucoup mieux o Je ne sais pas	o Timidité
	o Troubles de l'attention o Troubles digestifs o Non o Je ne sais pas Si oui, dans quel sens?	o Vertiges o Bégaiements
	o Palpitations Pouvez-vous décrire en quelques mots le(s) changement(s) lié(s) à la gestion de vos émotions ? o Angoisse o Moins bien
	o Hyperémotivité o Troubles de la mémoire o Allergies o Anxiété o Un peu mieux Si oui, dans quel sens ? o Beaucoup mieux Selon vous, est-ce que votre confiance en vous a changé après la pratique de la Méthode Quertant ? o Dépression o Obsessions o Moins bien o Cauchemar o Fatigue nerveuse o Un peu mieux o Oui o Non Pouvez-vous décrire en quelques mots le(s) changement(s) lié(s) à vos performances cognitives ?
	o Stress o Beaucoup mieux o Je ne sais pas	o Migraine
	o Dyslexie	o Autre(s) ____
	o Phobies Pouvez-vous décrire en quelques mots le(s) changement(s) lié(s) à votre vision ? Si oui, dans quel sens?
	Avez-vous ressenti des améliorations au niveau d'un ou plusieurs de ces symptômes/troubles? (Veuillez o Moins bien o Oui Examples: Augmentation/diminution du champ visuel, de la convergence, etc. o Un peu mieux o Non cocher toutes les réponses qui s'appliquent) o Beaucoup mieux o Je ne sais pas
	Selon vous, est-ce que votre personnalité a changé après la pratique de la Méthode Quertant ? Pouvez-vous décrire en quelques mots le(s) changement(s) lié(s) à votre confiance en vous ?
	o Oui
	o Non
	o Je ne sais pas

questions suivantes s'intéressent à votre expérience directe avec la Méthode Quertant.

  Durant votre pratique de la Méthode, quel(s) exercice(s) visuel(s) aviez-vous fait? o Avec les couleurs o Avec les lettres o Les deux Durant votre pratique de la Méthode, quel(s) exercice(s) visuel(s) vous a/ont paru plus les plus faciles

? (Veuillez cocher toutes les réponses qui s'appliquent) o

  Avec les couleurs, de loin ( Appareil de repos ) o Avec les couleurs, niveau intermédiaire ( Appareil effort moyen ) o Avec les couleurs, de près ( Appareil grand effort ) o Avec les lettres, de loin ( Appareil de repos ) o Avec les lettres, niveau intermédiaire ( Appareil effort moyen ) o Avec les lettres, de près ( Appareil grand effort ) o Avec Tous o Avec Aucun Durant votre pratique de la Méthode, quel(s) exercice(s) visuel(s) vous a/ont paru les plus difficiles? (

Veuillez cocher toutes les réponses qui s'appliquent) o

  Avec les couleurs, de loin ( Appareil de repos ) o Avec les couleurs, niveau intérmédiaire ( Appareil de repos ) o Avec les couleurs, de près ( Appareil grand effort ) o Avec les lettres, de loin ( Appareil de repos ) o Avec les lettres, niveau intérmédiaire ( Appareil effort moyen ) o Avec les lettres, de près ( Appareil grand effort ) o Avec Tous o Avec Aucun A la fin de votre pratique, aviez-vous réalisé des progrès au niveau de votre performance visuelle ? (

Veuillez cocher toutes les réponses qui o

  Avec les couleurs, de loin ( Appareil de repos ) o Avec les couleurs, niveau intermédiaire ( Appareil de repos ) o Avec les couleurs, de près ( Appareil grand effort ) o Avec les lettres, de loin ( Appareil de repos ) o Avec les lettres, niveau intermédiaire ( Appareil de repos ) o Avec les lettres, de près ( Appareil grand effort )

	o Avec Tous
	o Avec Aucun
	En général, étiez-vous satisfait(e) du résultat final ?
	o Oui
	o Neutre
	o Non
	Les

questions suivantes visent à connaitre votre niveau de satisfaction/insatisfaction par rapport à la Méthode Quertant.

  Très satisfaisante o Satisfaisante o Ni satisfaisante ni insatisfaisante o Insatisfaisante o Très insatisfaisante Cette Méthode vous a t-elle permis d'atteindre les objectifs que vous vous étiez fixés au départ?

	o Oui
	o Partiellement
	o Non
	o Pas du tout
	Conseillerez-vous cette méthode à proches?
	o Oui
	o Peut-être
	o Non
	Avez-vous d'autres commentaires/impressions concernant la Méthode Quertant que vous voudriez
	rajouter ?
	Globalement, que pensez-vous de la pédagogie utilisée ?
	o Très mauvaise
	o Mauvaise
	o Ni mauvaise ni bonne
	o Bonne
	o Très bonne

Avez-vous trouvé l'évolution de la rééducation satisfaisante ? o