Improving full-text search results on dúchas.ie using language technology

1 In this paper, we measure the effectiveness of using language standardisation, lemmatisation, and machine translation to improve full-text search results on dúchas.ie, the web interface to the Irish National Folklore Collection. Our focus is the Schools’ Collection, a scanned manuscript collection which is being transcribed by members of the public via a crowdsourcing initiative. We show that by applying these technologies to the manuscript page transcriptions, we obtain substantial improvements in search engine recall over a test set of actual user queries, with no appreciable drop in precision. Our results motivate the inclusion of this language technology in the search infrastructure of this folklore resource.

Collection , one of the largest collections of folklore in Europe, and a collection that contains material in both official languages of Ireland, Irish and English. Our proposition is that the full-text search facility available on the collection's website, dúchas.ie , can be enhanced 2 by introducing language technology options. By demonstrating that language standardisation, demutation, lemmatisation, and machine translation technologies can improve information retrieval on the website, this paper supports this proposition and motivates the inclusion of these technologies in the search infrastructure.

The National Folklore Collection
The Irish National Folklore Collection (NFC) is a large archive of folkloristic material collected in Ireland mostly during the 20th century, to which material is still being added. Part of the collection was inscribed into the UNESCO Memory of the World Register in September 2017. The NFC, which is located in University 3 College Dublin (UCD), aims to collect, preserve and disseminate the oral tradition of Ireland. 4 In 2012, the Dúchas project was established to digitise the collections of the NFC and publish them online. This project is a partnership between Dublin City University (DCU) and UCD (Ó Cleircín et al., 2014). The first major NFC collection digitised under the Dúchas project was the Schools' Collection . The Schools' Collection is a large collection of folklore stories collected from school children throughout Ireland between 1937 and 1939, as part of a state-sponsored scheme (Ó Catháin, 1988). The collection comprises approximately 740,000 manuscript pages.
Approximately 440,000 pages of the collection were digitised, manually indexed, and made available online on dúchas.ie between 2013-16. About 79% of the stories on these pages are in English (348,822 stories) and about 21% are in Irish (95,511 stories). These stories are enriched with various browsable metadata, e.g. title/excerpt, collector, informant, location, language.
Stories in the collection are also indexed by topic. The first part of this work was done during the initial field work. The Irish Folklore Commission prepared a guide for teachers participating in the scheme, and this guide was published as a handbook entitled Irish Folklore and Tradition (Irish Folklore Commission, 1937). This handbook contained 55 topic headings (e.g. a collection of riddles , local cures , the potato-crop , festival customs ), and teachers were instructed to collect material under these headings. Following the initial field work, researchers in the Irish Folklore Commission produced a topic-based index of the stories collected as part of the scheme. This paper-based index was based primarily on the 55 general topics from the handbook, but a large number of more specific topic headings (e.g. Fionn Mac Cumhaill , 1798 , warts ) were added, bringing the total number of topics up to c. 1,700. DCU digitised this index in 2014.
In 2016, using the digitised index and the story titles/excerpts indexed under the Dúchas project, and guided by the MoTIF Pilot Thesaurus of Irish Folklore (Ryan, 2015) developed by the Digital Repository of Ireland and the National Library of Ireland, DCU produced a shorter list of 208 standardised topic headings (e.g. riddles , folk medicine , potatoes , events ), and mapped them to the Schools' Collection stories, resulting in a new index for the Schools' Collection on dúchas.ie . This index is a mixture of broad headings (e.g. supernatural and legendary beings , events , folk medicine ) and narrow headings (e.g. banshees , Halloween , whooping-cough ).
In 2014, to facilitate full-text search of the collection, DCU initiated a project to crowdsource transcriptions of the Schools' Collection manuscript pages using a custom-built web-based application open to anyone (Bhreathnach et al., 2019). This project was conceived, in part, because of the problems associated with performing optical character recognition on the pages of the collection, which contain a mix of handwriting styles, a mix of scripts (i.e. Latin and Insular Celtic), and a mix of languages (i.e. Irish English and prestandard Irish).
The project has been a strong success with uptake amongst members of the public, students and folklore scholars. The transcriptions are generally of good quality and usable, in particular the Irish ones. Light editing is sometimes carried out, but bad transcriptions are rejected. Currently 49% of the English pages ( c. 170,000) and 32% of the Irish pages ( c. 31,000) have been transcribed. 5 The dúchas.ie website handles around 35,000 queries per month including around 16,000 full-text searches of the Schools' Collection transcriptions. All of these queries are logged in a database.

Irish standardisation
The orthography and grammar of the Irish language was standardised in the middle of the last century with the introduction of the Caighdeán Oifigiúil ('Official Standard'), first published in 1958 and revised in 2012 and 2017. Today, the standard form of the language is taught in schools, is used in all modern Irish dictionaries (both print and online), and has been almost universally adopted by the Irish-speaking public when writing the language. The spelling simplifications introduced by the standard cause occasional problems for Irish language technology, such as pairs of words that were distinguished in older orthographies which collapse to the same spelling in the standard (e.g. fiadhach ('hunting') and fiach ('a raven' or 'a debt'), both written fiach in the standard, or bádh ('a bay'), báidh ('sympathy, liking'), and bádhadh ('drowning'), all three written simply bá in the standard). Nevertheless, the overall effect of increased consistency and predictability arising from near-universal adoption of the standard has been a tremendous positive for the development of Irish language technology. It does mean, however, that taggers, parsers, and other NLP (natural language processing) tools developed for processing the standard form of the language fail badly when applied to prestandard texts. In the context of the current paper, the disconnect between the prestandard and standard orthographies makes it extremely difficult for users raised on the standard language to search corpora of prestandard texts such as the Schools' Collection.
An Caighdeánaitheoir ('The Standardiser') is an open source software package for standardising Irish texts, first developed around 2006, and detailed in (Scannell, 2014). The software treats standardisation as a machine translation (MT) problem between closely-related languages, employing a hybrid rule-based and statistical model trained on a large corpus of parallel prestandard and standardised texts (including, for example, many important novels and autobiographies first published in the 1920's and 1930's and manually standardised for a modern readership in recent years). It also attempts to correct misspellings before carrying out standardisation. The standardiser has been deployed by two important lexicographical projects in Ireland, the New English-Irish Dictionary project, and the Royal Irish 6 Academy's Foclóir Stairiúil na Gaeilge (Uí Dhonnchadha et al., 2014) to help both lexicographers and end-users search their corpora of prestandard texts more effectively.

Machine translation for Irish
While the 2012 META-NET study on Irish language technology resources (Judge et al., 2012) deemed Irish-language machine translation as being weak or not supported, in recent years we have witnessed some development in this field. Dowling et al. (2015) describe the development of MT for use within the translation workflow of an Irish government department, while Arcan et al. (2016) provide results of building a more general domain Irish MT system. Most recently, Dowling et al. (2018) compare the two main MT paradigms: statistical machine translation (SMT) and neural machine translation (NMT). Their results indicate that with a low-resourced language such as Irish, particularly when paired with a language that differs in terms of sentence structure and morphological richness, SMT may provide better results.
Building on this knowledge, we choose to duplicate the SMT system described by Dowling et al. for use in our experiment here. We train a phrase-based SMT system using Moses (Koehn et al., 2007), incorporating hierarchical reordering tables (Galley and Manning, 2008) in an attempt to address the divergent sentence structures (verb-subject-object in Irish, subject-verb-object in English). Our translation model is trained using the same 108,796 sentences of parallel data as in Dowling et al. (2018), coming from a variety of sources such as the Department of Culture, Heritage and the Gaeltacht, Conradh na Gaeilge and Citizens Information. We build a 6-gram, rather than the traditional 3-gram, language model using KenLM (Heafield, 2011) which also aims to reduce any negative impact of the divergent word orders. Our datasets are preprocessedsentence-tokenised, removal of blank lines, tokenisation of punctuation and truecasedbefore being translated by the MT system.

Methodology
Our principal aim in this paper is to investigate the effectiveness of standardisation and machine translation on the performance of the full-text search engine on dúchas.ie. We therefore cast this as an Information Retrieval (IR) problem. To this end, we set up experiments which make use of actual search queries submitted by users of the website, and aim to measure precision and recall of the search engine under various experimental conditions. Lacking a gold-standard corpus of relevant/non-relevant documents with which to measure precision and recall, we instead make use of a subset of the 208 topic labels, described in Section 1.1, attached to the stories in the Schools' Collection to create our own test corpus, as follows.
First, we manually examined the top 10,000 search queries submitted to dúchas.ie to date, keeping only the Irish language queries (just over 1,500 of the 10,000). Each of these was then manually compared with the list of 208 standardised topic headings, and we found 172 queries which clearly corresponded to some topic on the list; for instance, the topic Christmas was matched to the five queries "An Nollaig", "NOLLAIG", "Nollag", "nodlag", and "nodlaig". These 172 queries were matched to a total of 67 topics. We further restricted to those topics for which there were at least 100 transcribed stories in both Irish and English, leaving just 20 topics ( Riddles , Jokes , Fairy forts , The Great Famine , Entertainments and recreational activities , Folk medicine , Folk poetry , Food products , Religious tales , Clothing and accessories , Fianna , Feast of St Brigid , May , Halloween , Christmas , Prayers , Proverbs , Hardship , Graveyards , Potatoes ) and 72 of the original 172 queries. For each remaining topic, we randomly selected 100 Irish transcriptions and 100 English transcriptions from that topic in order to produce two test corpora of 2,000 documents each for our IR experiments. Finally, the English transcriptions were machine translated into Irish using the system described in Section 1.3 to allow us to evaluate the effectiveness of Irish language search queries for retrieving relevant English language documents.
The experiments differ only in the preprocessing that was applied to the documents in the test corpora before indexing and to the search queries before searching. The four experimental conditions are as follows: • Baseline : For this experiment, all text was converted to lowercase, and Irish diacritics (á,é,í,ó,ú) are converted to ASCII (a,e,i,o,u). This setup is the default behavior of the existing dúchas.ie search engine as of May 2019. • Standardised : For this experiment, the texts were standardised using the software described in Section 1.2, An Caighdeánaitheoir , and then lowercased and converted to ASCII as in the Baseline experiment.
• Demutated : Irish words are subject to so-called initial mutations which are triggered in certain semantic and syntactic environments and which cause the words to appear with different initial sounds. With rare exceptions, mutations in Irish can be detected and removed algorithmically in a trivial way because they are transparently reflected in the orthography (as is the case for Scottish Gaelic, but not for the other Celtic languages: Manx Gaelic, Welsh, Breton, and Cornish). For this experiment, the texts were standardised, lowercased, converted to ASCII, and finally all initial mutations were removed. • Lemmatised : Irish nouns and adjectives are inflected according to their case and number, and verbs are inflected according to tense, mood, person, and number. For this experiment, the texts were standardised, lowercased, converted to ASCII, and finally lemmatised by means of a lemmatiser which is part of the open source Irish grammar checker An Gramadóir . In the 7 case of verbs, the lemmatised form is the singular imperative, which is the usual citation form in modern Irish dictionaries.
For each experimental setup and for both test corpora, we preprocess the 2,000 documents in the corpus and the 72 search engine queries in our final list according to one of the schemes above. We then perform a full text search on the corpus for each of the 72 queries, recording the total number of returned documents and the number of those deemed to be "relevant" (here, by definition, relevant documents are those labeled with the topic corresponding to the given search query). For example, consider the query "An Nollaig". This search is typical of many others in that it conforms to the standard orthography most familiar to users of the site, and as a consequence it returns no hits at all in the Baseline experiment because the texts in the test corpus where this phrase appears all use the prestandard spelling "An Nodlaig". On the other hand, in the Standardised experiment, the same query returns 21 documents from the test set, with 19 of the 21 carrying the correct label "Christmas". This yields a precision of 19/21 ≈ 0.90 and a recall of 0.19 (19 of the 100 relevant documents in the corpus) for this one query.

Results and discussion
In Table 1 we report Precision, Recall, and F-scores for each of the experiments described in the preceding section as applied to the corpus of Irish transcriptions, totalled over all 72 search queries. Table 2 provides the analogous results for the experiments applied to the corpus of machine-translated English transcriptions. Since the MT engine produces standard Irish as its output, we do not report results for the Standardised setup separately in Table 2  Because the experimental setup is somewhat artificial, the absolute precision and recall values are not of great importance, but we do note in Table 1 a significant increase in recall over the baseline with the introduction of standardisation, and further increases with demutation and lemmatisation. These increases occur without any large decrease in precision.
In looking more closely at the results, a few things stand out. First, even with full standardisation and lemmatisation, a recall score of 0.34 seems low. This is due in part to the quality of some of the experimental search queries. For instance, a few of the 72 queries returned no results at all under any of the experiments. In one case, a search term was misspelled beyond the ability of the standardiser to correct it (" Oiche Shanmhna" for 'Halloween', correctly spelled "Oíche Shamhna" in the standard orthography). In another case, we generously interpreted the search query "ocras mór" (lit. 'great hunger') as an attempt to retrieve documents about the Irish potato famine, which indeed is sometimes referred to in English as "the Great Hunger", but for which the correct Irish is "(an) Gorta Mór" (lit. '(the) great famine'). Another class of low-recall queries appear to be searches for the topics themselves as opposed to searches for terms likely to appear in the full text transcriptions; e.g. the queries "filíocht" ('poetry'), "seanfhocail" ('proverbs'), "tomhaiseanna" ('riddles') all have very low recall values under all of the experimental conditions. This could be overcome by including the topic or other metadata in the search index.
The results in Table 2 show a similar increase in recall and no corresponding decrease in precision for the machine-translated English transcriptions. It is not surprising that the scores are somewhat lower than the ones in Table 1 since the machine translation engine is far from perfect and certainly introduces some noise into the process. On the other hand, the true baseline in this case is a recall of 0.0, since the current dúchas.ie search engine does not support retrieval of English transcriptions via Irish queries. We are therefore encouraged by these results, especially given that they were achieved through relatively straightforward use of existing language technologies.

Conclusion
We set out to improve full-text search results on dúchas.ie using language technology, building on crowdsourced transcriptions of folklore manuscripts. We have gathered together a set of existing language technologies to achieve this goal. These include tools to standardise, demutate, lemmatise, and translate the


Hunting for open clusters in \textit{Gaia} DR2: the Galactic anticentre

The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool to, for instance, search for hitherto unknown stellar structures. Particularly, supervised and unsupervised learning methods combined together allow to significantly improve the detection of open clusters. We systematically scan Gaia DR2 in a region covering the Galactic anticentre and the Perseus arm (120<=l<=205 and -10<=b<=10), with the goal of populating this region with open clusters and fine tuning the methodology, proposed in arXiv:1805.03045v2 and successfully applied to TGAS data, adapting it to different density regions. Our methodology uses an unsupervised, density based, clustering algorithm, DBSCAN, that identifies over-densities in the five dimensional astrometric parameter space (l,b,\varpi,\mu_{\alpha^*},\mu_{\delta}) that may correspond to physical clusters. The over-densities are separated into physical clusters (open clusters) or random statistical clusters using an artificial neural network to recognise the isochrone pattern that open clusters show in a colour magnitude diagram. The method is able to recover more than 75% of the open clusters confirmed in the search area. Moreover, we are able to detect 58 open clusters unknown previous to Gaia DR2, which represent an increase of around 25% with respect to the already catalogued ones in this region. We find that the census of nearby open clusters is not complete. Different machine learning based methodologies for a blind search of open clusters are complementary to each other, with no single method being able to detect the 100% of the existing groups. Our methodology has shown to be a reliable tool for the automatic detection of open clusters, designed to be applied to the full Gaia DR2 catalogue.

Introduction
Popularity of machine learning (ML) techniques to analyse astronomical data is growing together with the volume of astronomical catalogues. The use of these techniques is mandatory to extract meaningful insight from big data sets such as the second data release of ESA's Gaia astrometric mission (Gaia DR2, Gaia Collaboration et al. 2016, which contains a volume of more than 550 GB 1 of data, including precise astrometry (Lindegren et al. 2018) and excellent photometry (Evans et al. 2018), among other products, for more than 1.3 × 10 9 sources down to magnitude G = 21 mag. This unprecedented volume of extremely precise data reveals unseen details in the structure of our galaxy.
Open clusters (OCs) are considered to be fundamental objects in our understanding of the structure and evolution of the Milky Way disc. The stars of an OC were born and move together, i.e. in terms of Gaia observables, they share common (l, b, , µ α * , µ δ ) and follow a specific pattern in a colourmagnitude diagram (CMD) (G, G BP , G RP ). The fact that they represent over-densities in the five dimensional astrometric space can be exploited by unsupervised learning algorithms to either characterise known OCs looking for new member stars (Gao 2018a,b;Cantat-Gaudin et al. 2018), or to detect new overdensities in the parameter space (Castro-Ginard et al. 2018; 1 https://www.cosmos.esa.int/web/gaia/dr2 Cantat-Gaudin et al. 2019). Supervised learning methods can help in determining whether or not a group of stars is an OC by identifying the isochrone pattern of its member stars in a CMD, due to the common age of its members. In the OC domain, Gaia DR2 represents a perfect scenario for the application of ML methods to both its detection and characterisation.
Our understanding of the OC population has dramatically changed with Gaia DR2. A pre-Gaia census of the OC population counted with around 3000 objects (Dias et al. 2002;Kharchenko et al. 2013;Froebrich et al. 2007;Schmeja et al. 2014;Scholz et al. 2015;Röser et al. 2016) compiled from heterogeneous data sources, making the characterisation of OC parameters a difficult task. After the publication of Gaia DR2, Cantat-Gaudin et al. (2018) revisited the OC population using a ML based unsupervised membership determination algorithm. This resulted in the compilation of an homogeneous OC catalogue of 1229 objects, including some serendipitously detected OCs and discarding some objects listed in previous catalogues. These well-determined members and mean astrometrical parameters from Gaia DR2 data, allowed the kinematical study of these objects (Soubiran et al. 2018) and the derivation of ages and physical parameters (Bossini et al. 2019). Additionally, the combination of ML techniques and Gaia DR2 data triggered the detection of new OCs. The discovery of nearby OCs (Castro-Ginard et al. 2018;Cantat-Gaudin et al. 2019), where the census Article number, page 1 of 8 arXiv:1905.06161v1 [astro-ph.GA] 15 May 2019 A&A proofs: manuscript no. manuscript was thought to be complete, showed the necessity to keep exploring the sky for new objects.
In Castro-Ginard et al. (2018) (hereafter CG18) we presented a method for the automatic detection of OCs in the Gaia data. The method consists in the application of an unsupervised clustering algorithm, DBSCAN, that looks for over-densities in the astrometric five-dimensional space (l, b, , µ α * , µ δ ). Once the over-densities are detected, we classify them into either random statistical over-densities or real OCs by identifying the isochrone pattern of OC member stars in a CMD using an artificial neural network (ANN). The method has proved to be successful in the detection of OCs in the TGAS data (Lindegren et al. 2016;Michalik et al. 2015), that were later validated in the Gaia DR2 data. In this paper, we apply the methodology to a region of the sky around the Galactic anticentre with the aim of increasing our knowledge of the OC population in that region and fine tuning the methodology for its planned future application in an all sky blind search.
The paper is organised as follows. Section 2 briefly describes the methodology used, which is discussed in detail in CG18. The data-set used for the detection is described in Sect. 3. The proposal of new OCs and some comments on the results found are in Sect. 4. Finally, concluding remarks are summarised in Sect. 5.

Methodology
This section briefly describes the methodology used in CG18, where our approach to detect OCs in the Gaia DR2 data is explained in detail. The method consists in three parts: a preprocessing step, where the data is prepared to be exploited; the use of a density based clustering algorithm, DBSCAN (Ester et al. 1996), to look for over-densities in the 5-dimensional astrometric data; and a classification of the resulting clusters into real OCs and random statistical clusters using an ANN (Hinton 1989) to recognise the isochrone pattern of the cluster member stars in a CMD.
In the preprocessing step, the sky area of study is divided into smaller regions: rectangles of size L × L deg. The reason for that is to compute a representative averaged star density of the region where to search for over-densities. In each rectangle, the parameters (l, b, , µ α * , µ δ ), used to perform the clustering analysis, are standardised (re-scaled to have zero mean and variance one) to avoid a preferred dimension and to balance the importance of each dimension on the clustering process.
The detection of statistical clusters is done using the DBSCAN 2 algorithm, which is a density based algorithm that uses the notion of distance between stars to define close stars as a cluster. The statistical distance between two stars is computed as the euclidean distance in the standardised five dimensional parameter space. The reasons for the choice of DBSCAN are twofold. Firstly, it is able to detect arbitrarily shaped clusters so it accounts, for instance, for the effects of the projection of a cluster location into a two dimensional sky (l and b). Secondly, it requires only two input parameters: minPts, the minimum number of stars to consider them as a cluster; and , the radius of the hyper-sphere where to search for these minPts stars. The parameter is automatically computed in each rectangle using the fact that the distance between neighbours in a cluster is smaller than that between field stars (see Sect. 2.2 of CG18).
The values for the parameters (L, minPts) are set using Gaia DR2-like simulated data (see Sect. 3 of CG18), where in this 2 Algorithm from the scikit-learn python package (Pedregosa et al. 2011)  . The reddest the pixel, the better performance of the algorithm in terms of OC detection. Parameters selected, considered to be optimal for OC detection, are inside the black lines.
case we added the errors 3 at the time of Gaia DR2. Several combinations of optimal parameters (L, minPts) were selected in order to assess for the resulting performance of the algorithm, in this case we chose 28 pairs of (L, minPts). Figure 1 shows the pairs of parameters explored and the chosen combination inside the black lines, whose values range within L ∈ [9 • , 13 • ] and minPts ∈ [8, 15]. The selection of these parameters was made trying to find a balance between low noise and good efficiency, defined as the false positive -true positive ratio for the noise and false negative -true positive for the efficiency. After the clustering process, the resulting clusters can be either real OCs or random statistical clusters. This two types can be differentiated by the pattern followed by the cluster member stars on a CMD. The classification into real OCs or random statistical clusters is done with an ANN 2 , which is able to identify the characteristic shape of isochrones in CMDs corresponding to real OCs. To train the ANN we used CMDs from OCs from the most homogeneous OC catalogue to date (see details in Cantat-Gaudin et al. 2018), which also has the advantage of being compiled from Gaia DR2 data so it is representative of the OCs we expect to detect, and with similar photometric errors. The training set consists in a sample of 1229 real OCs. In addition we used data augmentation techniques so the volume of the training set was increased by randomly selecting member stars to create a set of sub-clusters from each of these catalogued OCs. On the negative identification side, we used CMDs from random field 3 Implementation provided by PyGaia package: https://github.com/agabrown/PyGaia stars on the same field that those 1229 OCs to avoid location biases.
As a last step, and to ensure the selection only of newly detected OCs, we remove the already catalogued OCs. We improved this step with respect to CG18 thanks to the compilation of the catalogue by Cantat-Gaudin et al. (2018). In this case, not only positional arguments were use in order to match a found OC with a catalogued one. An OC was considered to be already catalogued if the mean parameters (l, b, , µ α * , µ δ ) of its members was compatible within 2σ of those mean parameters of the catalogued OC in Cantat-Gaudin et al. (2018). We did not make a cross-identification with other catalogues such as Dias et al. (2002); Kharchenko et al. (2013); Bica et al. (2019) and others, due to the in-homogeneous data sources they are compiled from.

Data
The Gaia catalogue, in its second data release (Gaia DR2, Gaia Collaboration et al. 2018), provides precise five-dimensional astrometric data (positions, parallax and proper motions) together with magnitudes in three photometric broad bands (G, G BP and G RP ) for more than 1.3 billion sources up to G = 21 mag. In this work, we focused in a region located at the disc (b ∈ [−10 • , 10 • ]) near the Galactic anticentre (l ∈ [120 • , 205 • ]) down to magnitude G = 17 mag, where we find a total of 8 715 057 sources with mean standard uncertainties of 0.07 mas for the parallax and 0.1 mas·yr −1 for proper motions.
The reason we fixed the search region in the Galactic disc is because the expectation to find OCs decreases at higher altitudes. For instance, around 93% of the OCs catalogued in Cantat-Gaudin et al. (2018) are at |b| < 10 • , and around 99% are located at |b| < 20 • ; similar numbers are found in the catalogues of Dias et al. (2002) and Kharchenko et al. (2013) with 96% and 94% of the OCs located at |b| < 20 • . Moreover, initially the search region was as wide as |b| < 40 • but an exploratory analysis of the results of our method showed that the detection of clusters tend to be less reliable at |b| > 10 • . This effect is shown in Fig. 2, the clusters at |b| > 10 • are detected less number of times within the 28 pairs of (L, minPts) explored than those located at the disc, decreasing the reliability of the candidate. In addition, clusters detected outside the disc increase in size with Galactic latitude, so with decreasing stellar density. Since there is no physical reason for this and although we cannot discard that some of those detected clusters may be real, we interpret that the determination of the parameter for such low density regions is not accurate and therefore we decided to limit the final search region to the disc, defined as |b| < 10 • .
The reason for the choice of the region near the Galactic anticentre is twofold. On the computational side, the limited volume of data due to the manageable density of stars in the anticentre direction facilitates its analysis while keeping the richness of the data up to G = 17 mag. On the astrophysical side, more distant objects can be reached due to the moderate extinction caused by interstellar dust, compared to the Galactic centre direction. The search region also covers the area recently studied by Cantat-Gaudin et al. (2019) with Gaia DR2 data, where they find 41 new clusters and notice that the region l ∈ [140 • , 160 • ] seems to be devoid of OCs.

Results
The method described in Sect. 2 is applied to the Gaia DR2 data, focused in a region around the anticentre, i.e. 120 • ≤ l ≤ 205 • ; The grey-scale represents how many times each cluster is found within the pairs of (L, minPts) explored. High latitude clusters are detected fewer times and are larger in size.
and in the disc, −10 • ≤ b ≤ 10 • . This results in the detection of 58 OCs that were unknown previous to Gaia DR2, which represent an increase of ∼ 25% with respect to the reference catalogue.

Determination of a detection
We can assess the detection criteria by comparing the detected and non-detected OCs from the existing catalogues. In our region of search, Cantat-Gaudin et al. (2018) report 240 OCs of which we are able to recover 182, i.e. ∼ 76% of the already known OCs. The reason for the non-detection of the remaining ∼ 24% OCs is related to the contrast of the OC with respect to the field, as seen by the DBSCAN algorithm. Figure 3 shows a distribution of the parameter computed for each of the 240 OCs, including both the detected (blue dots) and non-detected OCs (red dots), for L = 13 • and minPts = 9. The computation of the parameter, as explained in Sect. 2.2 of CG18, is done via a data-driven approach so for the interpretation of the parameter not just the physical properties of the OCs (or the field) have to be taken into account but the whole data-set used instead. In this case, the key factor that enables the detection of the OC is the OC-field contrast in terms of compactness. We see from Fig. 3 that only clusters with low values of (high contrast) are detected. This is confirmed by the fact that the reapplication of the method detects most of the undetected OCs when increasing the contrast with respect to the field by localising the search area to a cone search centred at the targeted OC, instead of the large rectangle.

Proposal of new OCs
The application of our method to the described data-set leads to an initial list of 496 OC candidates. The Monte Carlo type analysis (application of the method for several optimal pairs of parameters) allows us to assess the reliability of these detections by the number of times each cluster was found. In order to clean the initial list from false positives, we manually inspect each of the OC candidates and try to re-detect the candidate in a smaller field (cone search around the centre of the targeted OC) where the OC-field contrast is higher. This re-detection was done using again the DBSCAN algorithm in a cone search region centred in the targeted OC 4 . The decision on the proposal of the candidate as an OC is made based on the reliability of the candidate and its re-detection.
After this manual step, 58 of these 496 are validated and proposed as OCs. The reason for the validation of only these 58 OCs is related to the low complexity of the ANN's architecture used, which is so due to the low volume of training data available (based on Gaia DR2 data only). This can give false positive identifications, i.e. wrong identification, by the ANN, of a stellar structure as an OC. In our manual validation step we have been conservative, tending to accept as OCs those groups without a sparse distribution in the sky, with the larger compactness in proper motions and parallax and with the better defined sequences in the CMD. This step may have introduced a strong bias in the selection and rejected true clusters. With the improved proper motions and parallaxes of Gaia DR3 and a more populated training data-set, one will be able to repeat the analysis to fainter magnitudes and this will allow to have less dubious cases. Even though the method is devised to require the minimal user intervention, this is an important step as the exploitation of the Gaia data in terms of blind search for stellar structures is at its initial stages, so a robust OCs catalogue needs to be built to reliably train an automatic detection procedure.
A final list of 58 OCs is proposed, divided in class A OCs and class B depending on the reliability of the candidate. Positions (α, δ) and (l, b) together with mean parameters ( , µ α * , µ δ ) and mean V rad when available can be found in Table 1 for each of the new OCs, which also includes the computed apparent size of the OC and its estimated distance with one sigma (asymmetric) confidence interval. A list of the detected members for all the reported OCs is available in Table 2

Comments on the detected OCs
The newly found OCs are distributed along the Galactic anticentre direction as shown in Fig. 4, where green crosses represent OCs found in this work, blue triangles are the already catalogued OCs in Cantat-Gaudin et al. (2018) and yellow boxes are the OCs in Cantat-Gaudin et al. (2019). It is worth noting that in a region around l ∼ 140 • the density of OCs decreases in terms of both catalogued clusters and newly detected ones. This confirms the findings in Cantat-Gaudin et al. (2019) that this region seems to be devoid of OCs. The low OC density is better seen in Fig. 5, where an X-Y projection is shown with the Sun at (0, 0), and it seems to be pointing in the direction of the Perseus arm (Local and Perseus arms follow the model of Reid et al. (2014)). This region of relatively lower density was first reported as a lack of OB stars in the Gaia DR2 data, and dubbed Gulf of Camelopardalis 6 .
Our strategy to detect OCs rely in the OC-field contrast, being able to detect those OCs with the highest contrast. This may result in a detection bias towards the more compact objects. Figure 6 shows the radius of the detected OC as a function of its distance, which is computed as 1/  given the low parallax relative error (σ ∼ 0.04 mas corresponding to 3 − 16% in parallax relative error). The size range of the objects found is increasing with distance, limiting our detection to very compact objects in a close neighbourhood. The mean size of the detected OCs is σ l , σ b ∼ 0.08 • corresponding to an apparent size of θ ∼ 0.11 • . Our detection limit seems to be at a cluster apparent size of θ = 0.2 • .
In terms of estimated distance, we find 7 new OCs within 1 kpc (being the closest one at around 645 pc) and 31 within 1.8 kpc, to be added to the 23 of Castro-Ginard et al. (2018) and the 31 of Cantat-Gaudin et al. (2019) found in that distance range, further supporting the claim that more objects are yet to be discovered in this volume, specially with the combination of the excellent Gaia data and ML algorithms in future all sky searches. This challenges the statement that the OC census is complete up to 1.8 kpc (Kharchenko et al. 2013;Matsunaga et al. 2018;Piskunov et al. 2018).
From the kinematical point of view, the reported OCs have a mean dispersion of µ α * , µ δ ∼ 0.2 mas·yr −1 , computed from the found member stars. This corresponds to a mean tangential velocity dispersion of ∼ 2.2 km·s −1 . Only 35 of the 58 reported OCs have a radial velocity measurement available in Gaia DR2, with 14 of them having more than 2 measurements (N V rad > 2). The large σ V rad for 6 of them may indicate the presence of binaries or non-members. Cross-matching with external surveys dedicated to radial velocity estimation, such as APOGEE (Majewski et al. 2017), does not add information (only one star was found in common between the two catalogues). The little information on radial velocities difficulties the characterisation of OC members free of contamination from field stars.
The photometric information is included when deciding if a CMD matches a real OC or not. This is done using an ANN trained with CMDs from the 1229 OCs in Cantat-Gaudin et al.
(2018) (see Sect. 2), so the expected isochrone patterns are similar to those present in the training set. The ages of the reference clusters used in the training span from 40 Myr to 1.5 Gyr, so objects accepted by the ANN are in that age range. No estimation of photometric derived quantities is done here, only to mention that 28 of the 58 reported OCs have stars evolved beyond the main sequence, representing the oldest population of the found clusters. In Fig. 7 few examples of detected OCs are shown, 4 class A and 1 class B, showing different ages. Together with the Table 1: List of the proposed OCs ordered by increasing l. The parameters shown are the mean (and standard deviation) for the (N) members found also including the computed apparent size (θ) and estimated distance (d) with one sigma confidence interval, radial velocity is included when available and it is computed with N V rad members. The name follows the numeration started in CG18.

Matches with other catalogues
The candidates have been cross-matched with known catalogues of OCs (Dias et al. 2002;Kharchenko et al. 2013). These catalogues contain around 2000 and 3000 known stellar structures, respectively. However, some of these structures have been recently found not to be real OCs ( as σ 2 l + σ 2 b ) as a function of distance for each of the reported 58 OCs. Dotted lines represent the limiting cluster apparent size and the mean apparent size, θ = 0.2 • and θ ∼ 0.11 • , respectively. data sources, making the identification of an OC less reliable beyond positional arguments. We consider an OC to be positionally matched to a catalogued one if their centres lie within a circle of radius r = 0.5 • . We find 17 candidates which centres are identified with one object either from Dias et al. (2002) or Kharchenko et al. (2013), however none of the identifications are compatible in the rest of the astrometric mean parameters ( , µ α * , µ δ ), with the closest pair differing by ∼ 8σ in at least one parameter. However, we find UBC 84 near the association Cas OB1, to which it may be related due to the extended region of an association.
A recent list of 10 978 star clusters, associations and candidates in the Milky Way has been published by Bica et al. (2019). Our list of candidates was cross-matched and only UBC 90 and UBC 44 are near one entry in the catalogue, Teutsch 20 and Patchick 12 respectively. Teutsch 20 and Patchick 12 are not listed in any of the other studied catalogues. Moreover, the quoted distance for Teutsch 20 is 2.54 ± 0.05 kpc (Guo et al. 2018) and we find UBC 90 at 2.94 kpc, not being compatible within errors. For the case of Patchick 12, we found no record of its mean astrometric parameters in the literature. Leftmost plots show the position of the OC in (α, δ). Inner left plots show (µ α * , µ δ ) distribution whilst inner right plots show the distribution in ( , µ α * ). Rightmost plots show the CMD of each OC. The plotted OCs are, from top to bottom: UBC 54, UBC 56, UBC 57, UBC 79 and UBC 90. The first four clusters are class A and the last one is a class B cluster (see Table 1).
As said before, Cantat-Gaudin et al. (2019) recently found 41 OCs located in roughly the same area of the sky, exploring the data of Gaia DR2. We find 21 OCs in common, that share the five astrometric parameters (see Table 1). The other 20 OCs were not detected in our blind search, but we were able to recover them by increasing the OC-field contrast when running DBSCAN in a cone search centred in the targeted OC. This shows that ML methods are complementary to each other, with none of the explored methods being able to detect the 100% of the existing structures.

Conclusions
We use the methodology described in CG18 to systematically explore the Gaia DR2 archive to search for unknown OCs in the anticentre direction. The method is a fully automated data mining task consisting in an unsupervised clustering algorithm, DB-SCAN, to find groups of stars that share common (l, b, , µ α * , µ δ ) and decide whether or not they are real OCs based on an isochrone pattern recognition in the CMD using an ANN.
We can assess the overall performance in terms of the detection of already existing OCs. In this case, the method is able to find more than 75% of the confirmed OCs in the search region. Most of the remaining ∼ 24% of the clusters not found are recovered when the search is focused in the targeted OC. This suggests that our method works better for the OCs which OC-field contrast is high, and may be biased towards the most compact objects when the distance decreases.
The application of the whole methodology leads to the report of 58 new OCs in a region covering the Galactic anticentre and the Perseus arm in the Gaia DR2 data (120 • ≤ l ≤ 205 • and −10 • ≤ b ≤ 10 • ), which represents an increase of around 25% with respect to the OCs catalogued in this area. Moreover, 32 of the detected OCs are closer than 2 kpc, suggesting that there may be more groups to be detected in this volume.
The density of OCs decreases in a region near l ∼ 140 • . Very few OCs are found in this region, including already catalogued OCs and the newly reported. This region has been named as the Gulf of Camelopardalis, and it reveals a complex structure of the second Galactic quadrant whose mapping was only recently made possible by Gaia DR2 data, and still deserves further study.
The application of our methodology in the search regions shows that the census of OCs may not be complete. Moreover, other similar methodologies exploring the same region are able to find more groups not detected in our method, while they missed some groups detected here. We conclude that a blind search using a single detection method could not recover all the existing stellar structures and that different ML algorithms for this purpose are complementary to each other.
The design of the whole methodology, requiring minimal manual intervention, makes possible its application to a big data set such as the whole Gaia DR2. The planned future exploitation of the Gaia archive in terms of blind search of OCs would represent a huge increase to the known OC population.


extfieldsofstudy:
Computer Science
Mathematics

text:
A New Class of Scaling Matrices for Scaled Trust Region Algorithms

A new class of affine scaling matrices for the interior point Newton-type methods is considered to solve the nonlinear systems with simple bounds. We review the essential properties of a scaling matrix and consider several well-known scaling matrices proposed in the literature. We define a new scaling matrix that is the convex combination of these matrices. The proposed scaling matrix inherits those interesting properties of the individual matrices and satisfies additional desired requirements. The numerical experiments demonstrate the superiority of the new scaling matrix in solving several important test problems.

I. INTRODUCTION
Diverse applications including signal processing [36] and compressive sensing [3,4] incorporate many convex nonlinear optimization problems. Although specialized algorithms have been developed for some of these problems, the interior-point methods are still the main tool to tackle them. These methods require a feasible initial point [19]. It should be paid attention that proper data collection plays an important role in assessing the results obtained [39]. In addition, a vast variety of soft-computing techniques such as evolutionary computing methods [2,18,28,37,38] and neural networks [1,31] include optimization problems, which are sensitive to the initial points. Like verifying solution uniqueness conditions, these tasks convert into linear feasibility problems with strict inequalities or nonlinear feasibility problems with bound constraints [16,34]. The latter is often challenging so that the existing algorithms need to be theoretically and computationally improved. The nonlinear minimization problem with bound constraints is: Efficient methods for the solution of this problem with good local convergence behavior have been proposed. The affine scaling trust region approach forms a practical framework for smooth and nonsmooth box constrained systems of nonlinear equations [6][7][8][9]. These kinds of methods use ellipsoidal trust region defined by a diagonal scaling matrix [12]. The diagonal scaling handles the bounds while at each iteration a quadratic model of the object function 1 2 || F || 2 is minimized within a trust region around the current iteration.
The main motivation for the current work is a series of papers by Bellavia et al [6][7][8][9][10][11]. The methods they introduced, STRN and CODOSOL have very good numerical properties [6,7]. These methods are widely used in practice [14,35] and their efficiency has been proved in several papers [7,23]. In [8] the authors studied global and fast convergence of an inexact dogleg method. They did not investigate the choice of a suitable scaling matrix and only reported the preliminary results. Later in [11] they focused on medium scale problems and replaced the inexact Newton step by the exact solution of the Newton equation. They considered several diagonal scaling matrices and showed the assumptions required to ensure the convergence. The name of the method is Constrained Dogleg (CoDo) method which is freely accessible through the website http://codosol.de.unifi.it. The effectiveness of (CoDo) is verified by comparing it to STRSCNE [7] and to IATR [9].
In these scaling-based algorithms, the performance is influenced by the selection of a scaling matrix. We introduce a new class of scaling matrices, which is obtained by the convex combination of current known matrices. We analyze the numerical performance of CoDoSol (The Matlab Solver of CoDo) for different convex combinations of the scaling matrices and compare the results using performance profile approach. We also use a Projected Affine Scaling Interior Point algorithm to check the local convergence properties of the new scaling matrix.
In section 2 we explain the role of the scaling matrices. In section 3 we consider several scaling matrices and review the requirements and assumptions. In section 4, we introduce a new class of scaling matrices and prove that the new matrices satisfy the required assumptions. Finally, in section 5 we report our conclusion of computational results.

II. SCALED TRUST REGIONS
In this section we describe the idea behind using scaled matrices to solve problem (1). First note that (1) is closely related to the box constrained optimization problem: Every solution of (1) is a global minimum of (2.2) and if x * is a minimum of (2) such that f (x * ) = 0 , then x * is a solution of (1). The first order optimality conditions of (2) are equivalent to the nonlinear system of equations and D is a suitable scaling matrix of order n Coleman and Li [12] considered only one choice of the scaling matrix, then Heinkenschloss et al. [21] noted that the optimality conditions holds for a general class of scaling matrices satisfying the conditions: (4) for all i=1,...,n and all x Î W. In affine scaling methods in order to handle the bounds the direction of the scaled gradient is defined by Given an iterate x k Î int (W) and the trust region size D k > 0, the trust region subproblem for (2) is: where m k is the norm of the linear model for 2 the elliptical trust region achieves. In order to solve this subproblem different approaches have been proposed like STRN [6] which combines ideas from the classical trust-region Newton method for unconstrained nonlinear equations and the interior affine scaling approach for constrained optimization problems or CoDoSol [11] which is based on a dogleg procedure tailored for constrained problems.

III. SCALING MATRICES
We consider the following well known matrices: • D CL (x ) given by Coleman and Li [12]. The diagonal elements are:   a(x k ) = l k . Bellavia et al in [11] introduced the following assumptions for a scaling matrix and verified that all above scaling matrices satisfy almost all the requirements specified by the following assumption.

A. Assumption
We define the convex combination of the above scaling matrices as a new class of scaling matrices as follows: This scaling matrix demonstrates the advantages of all the scaling matrices involved in its definition and seems an appropriate matrix with combined properties. we first have to verify that this matrix satisfies four desired requirements as follows (i) Clearly D CON satisfies (4). (ii) As Bellavia et al. [11] showed, the above three matrices are bounded in x Î W and r > 0 . This implies that the combination of these matrices is also bounded.
(iii) It has been shown in [11] that all the matrices except D HUU join this property. Thus if we assume a 2 = 0 then D CON satisfies this condition.
(iv) Same as before, since all the matrices satisfy condition (iv), the convex combination also verifies this property.
We implement the Constrained Dogleg method in the Matlab code CoDoSol using Elliptical trust-region with initial radius of 1. The limiting number of the iterations is set to be 300 and the limiting number of F-evaluations is set to be 1000. The experiment was on 15 problems with dimension between n=2 and n=1000 specified in Table 1.
Different types of constrained systems including systems with solutions both within the feasible region and on the boundary, systems with only lower (upper) bounds and systems with variable components bounded from above and below can be found in this table.
Nonlinear constrained systems come from [13,15] (problems Pb1 to Pb6), [40] (problems Pb10 to Pb13), chemical equilibrium system given in [17,32,33,41] (Pb7), and nonlinear complementarity problems (NCPs) given in [25,29] (Pb8, Pb9). While Pb15 [20] comes from nonlinear BVPs. Dealing with large dimensional problems is also critical, these problems need more CPU and, in some cases, they need to be reformed before feeding them into the solver [5]. Problems Pb14, Pb15 are examples of this kind of problems [30]. The starting points for the problems with finite lower and upper bounds are selected by a uniform distribution between l and u i.e. In CoDoSol the trust region size is updated as in [10,11], the failure criteria and parameter selection is same as [24] where the authors introduced an innovative and efficient method for parameter selection. We tested the algorithm with the scaling matrices and reported the numerical results. Thus, the CoDoSol is tested with the following scaling matrices: (11) and The efficiency of the scaling matrices has been measured by It, the number of the iterations and Fe, the number of the function evaluations to get convergence ( Table 2,3,4).
Bellavia et al showed that CL is superior to KK and KK is superior to HUU on their studied test problems, in our test problems KK is slightly better while HUU shows the poorest performance. HUU shows as good performance as or better performance than the individual matrices.
To compare these seven scaling matrices, we use the performance profiles and to have a more reliable comparison we used Nested Performance Profiles [22], which removed a negative side effect of the performance profiles. In Fig 1 and Fig 3 the computational effort is measured in terms of mean It (the mean number of iterations for the three starting points) and mean Fe (mean F-evaluations for the three starting points) respectively. In these figures we eliminate the convex coefficients and show 1 The black doted lines correspond to the individual matrices and the red line corresponds to the convex combination of the scaling matrices.
By looking at the performance profiles corresponding to CL+HUU we can see that it is efficient in solving about the 75% of the tests and solves about 95% of the tests within a factor 1 from the best solver. CL+HUU is the best scaling matrix for the studied problems.
This can be verified by looking at the figure 2 and figure 4. Figure 2 is based on It while Figure 4 is based on Fe.    In this section, we illustrate the local behavior of the different scaling strategies using two standard test problems. We implemented Algorithm "Projected Affine-Scaling Interior-Point Newton Method" [29]. The first test example is the famous Rosenbrockfunction: This function has a unique global minimum at 1) . The lower and upper bounds are l = (0,0) and u = (1,1).
Again, we see that the convergence of the Coleman-Li matrix is rather slow. While according to Bellavia et all it is the fastest one for the medium scale problems. Clearly the convex combination of the CL and KK helps it to mitigate the violation of the strict complementarity assumption that slows down the convergence rate of the affine-scaling Newton method using the Coleman-Li scaling. During our long implementations it has been proved that the performance of the scaling matrices depends on the test problems. Each matrix has its own advantages and disadvantages. For a new problem, there is no way to select the best possible scaling matrix and it has to be done by trial and fail. By changing in the dimension of the problem or changing the parameter values of a problem the previous matrix will not necessarily work as the best one. Also the performance of the scaling matrices highly depends on the algorithm. A matrix demonstrates fast convergence for a specific algorithm and slow convergence for other algorithm. In order to overcome this problem, one can use the convex combination of the scaling matrices. This new class of scaling matrices has the advantages of the individual matrices and in some cases works as the best option.


s2fieldsofstudy:
Computer Science
Mathematics

text:
Enhanced Security of Symmetric Encryption Using Combination of Steganography with Visual Cryptography

Data security is required when communications over untrusted networks takes place. Security tools such as cryptography and steganography are applied to achieve such objectives, but both have limitations and susceptible to attacks if they were used individually. To overcome these limitations, we proposed a powerful and secured system based on the integration of cryptography and steganography. The secret message is encrypted with blowfish cipher and visual cryptography. Finally, the encrypted data is embedded into two innocent cover images for future transmission. An extended analysis was made to prove the efficiency of the proposed model by measuring Mean-Square-Error (MSE), Peak-Signal-to-noise-Ratio (PSNR), and image histogram. The robustness was examined by launching statistical and 8-bit plane visual attacks. The proposed model provides a secure mean to transmit or store highly classified data that could be applied to the public security sector.

I. INTRODUCTION
Data is the major part of any communication system. Computer networks provide good means to transfer these data. In some cases, private data have to be sent through networks which might be insecure due to vulnerable network segments such as wireless access points or because of computer crackers. Therefore, the need to information security has increased as the types of attacks on the data networks are also increased. There are many tools applied to keep private data safe from unauthorized access. Cryptography and Steganography are the most common tools used to achieve this level of protection. Cryptography is the art and science of using mathematical functions to transform data from their clear readable form to another scrambled form to unauthorized parties [1]. Only the intended users with additional secret information, called key, can read or access the data.
On the other hand, Steganography is the art and science of hiding the existence of secret messages into another innocent source of information such as multimedia files [2]. It does not encrypt the secret messages, but only hides them. Both cryptography and steganography have their advantages and limitations, using them individually will provide a single layer of security which could be easily compromised. Integrating them together will provide additional layers of security and their limitations will be reduced.
It uses a variable-key length from 32 and up to 448 bits. Blowfish is a Feistel network with 16 iterations. Two operations are forming the blowfish algorithm; key expansion and data encryption. Key expansion converts user"s key into several sub-keys P-array and S-Boxes which will be used later in encryption and decryption. With total of 4168 bytes both P-array and S-boxes are described as follows [4]: P-Array of 32 bits length for each entry: P1, P2…, P18. 4 S-Boxes of 32 bits length for each entry: S 1,0 , S 1,1 ,…,S 1,255 S 2,0 , S 2,1 ,…,S 2,255 S 3,0 , S 3,1 ,…,S 3,255 S 4,0 , S 4,1 ,…,S 4,255 As shown in figure 1, the Blowfish Algorithm is summarized in the following procedure: The input x is an 64-bit block data element Divide x into two 32-bit halves: xL, xR For i = 1 to 16   Like the other Feistel network ciphers, blowfish uses the generated sub-keys in reversed order to decrypt ciphertext.

B. Visual Cryptography
A. Shamir and M. Naor [13] proposed a new cryptographic scheme based on secret sharing namely, visual cryptography. In (k, n) visual cryptographic scheme, a dealer encodes a secret image into n shares and gives each participant a share, where each share is printed on a transparency. The secret will be revealed if any k or more of the permitted shares stacked together. No clue about the original secret will be revealed if fewer than k shares are used. As illustrated in figure 3, an (2, 2) visual cryptographic scheme generates two shares. Both shares are required in order to reveal the secret back, To share a white pixel, the dealer randomly chooses one of the upper rows combinations of black/white sub-pixels, and to share a black pixel, the dealer randomly chooses one of lower rows, Figure 4. The decryption is done by stacking sub-pixels together. This process is equivalent to the OR operation to these sub-pixels. XOR operation might be used to enhance the contrast of the superimposing of the two shares. The result of stacking sub-pixels is the shown in the right column of figure 4 [14].

C. LSB Image Steganography
Steganography hides the secret message into another cover message. It can be applied to computer files such as document files, images, applications and communication protocols. Media files are good choice as cover objects [2]. As shown in figure 5, any steganographic system consists of three main parts, the secret message m to be hidden, the cover object C which is the carrier medium to hide m, and the Stegoobject S the output of the embedding process. Least Significant Bit (LSB) algorithm conceals the secret message into cover medium through altering every LSB of the cover pixels with each bit of the secret bits [15]. As example to hide a secret message "S' into an image will look like this: After embedding the secret 'S' into the cover image, the new pixels values will be: (1011011, 1100001, 1100011, 1100010, 1100100, 1100101, 1100011)

Fig 5: LSB Steganography
This paper is organized as follows; section 2 presents the related work. Section 3 describes the encryption and decryption phases of the proposed model. Section 4 summarizes the results and discusses the performance. Finally, section 5 concludes the paper.

II. RELATED WORK
Many researches focus on the advantages of combining both of cryptography and steganography. This hybrid approach could be classified into two main categories; the secret message is first encrypted then embedded into carrier medium. The other approach embeds the secret message firstly into the carrier medium then encrypts them.
H. Y. Atown [3], proposed a model to secure fingerprint images by first encrypting it with transposition algorithm then hides the scrambled image into a coloured cover image using LSB. This model produced an PSNR value above 50dB. B. Prasad, K. C. Lakshmi Narayana [4], proposed another combined scheme uses genetic steganographic algorithm to hide secret data followed by visual cryptography to add extra layer of security for the system. It works only with bitmap images and the compression depends on the document size as well as the size of the cover image. Another limitation of their model is that transmitting the noisy shares will raise the suspicion of the adversary against the intercepted images.
P. H. Dixit, K. B. Waskar, U. L. Bombale [5], introduced a multilevel network security by blend of encrypting the data with blowfish algorithm then applying LSB steganography on small embedded systems. This algorithm uses biometric features of the sender as a key for encryption. It uses the iris image for generating sub-keys for blowfish algorithm. The ciphertext is to be hidden into the iris image with LSB method before sending over network to the receiver side. The main limitation noticed is sending the iris image which was used for generating the subkeys of blowfish algorithm. Also, with the iris image it solves the visual analysis of Stego-objects, but it cannot guarantee the statistical attacks over it.
M. Gupta, D. Chauhan [6], proposed a hybrid model which uses a (2, 2) visual cryptographic scheme along with digital watermarking to hide the generated shares. It solves the flaw of visual cryptography that could happen if an attacker alters the bits of these shares with fake bit sequences by digital watermarking.
T. S. Barhoom, S. M. Abo Mousa [7], introduced a steganographic algorithm could be applied to grey-scale and coloured images based on LSB technique. The proposed scheme aims to provide more security to the hidden information, so blowfish encryption is applied before the embedding process is done.
M. D. Munjal [8], proposed a new multilayered security scheme based on applying dual steganographic algorithms LSB and discrete wavelet transform (DWT). The secret message is embedded into two cover images. It adds more security along with good quality of the final steg-image (high PSNR, low MSE). The noticed drawback is applying statistical steganalysis on the stego-image and probably extraction of the hidden message as it is embedded as a plaintext.
R. Rathod, D. Mistry, K. Patel [9], introduced another combined approach to provide more security through first encrypting the secret message with matrix reorder or pixel shuffling to produce a scrambled version of the secret message. After that, blowfish is applied for extended encryption. Then spiral LSB method is used to hide the encrypted shuffled image. P. K. Kavya, P. Haseena [10], proposed an efficient system for fingerprint login mechanism to authenticate users. This algorithm depends on visual cryptography and video steganography along with user input biometric information (fingerprint). Blowfish cipher is used to encrypt the fingerprint image before embedding in the video cover.

III. PROPOSED MODEL
The hybrid of cryptography and steganography provides multilevel security to the data. The proposed system is based on Blowfish symmetric key and visual cryptography encryption ciphers along with LSB substitution steganography. The following sub-sections describe the encryption and decryption phases involved in the proposed model.

A. Encryption Phase:
The encryption phase consists of three modules E1, E2, and E3. In E1 the secret message m is encrypted with the Blowfish algorithm resulting ciphertext C. For an extra level of security, the fingerprint of the user, KIMG, is used to generate sub-keys required in encryption rounds.
C is then converted into an image, ENCIMG, which will be supplied to the second module E2, where an (2, 2) visual cryptographic scheme is applied.
After the two noisy shares, SH1and SH2, are generated, they will be concealed within two innocent cover images using LSB steganographic algorithm producing two Stego-images, STEG1 and STEG2.
Later these Stego-images will be sent through two different insecure communication channels to the receiver side.
As illustrated in figure 6, the encryption procedure is summarized in the following steps:

B. Decryption Phase:
The decryption phase is also consisting of three modules D1, D2, and D3. After receiving the two Stego-images, STEG1 and STEG2, the D1 module extracts and reconstructs the embedded noisy shares producing RSH1 and RSH2. After then, they will be provided to D2 module in which the decryption of VCS takes place to restore the ciphertext C.
The decryption is achieved through superimposing both shares together using the XOR operation. The other part of D2 module is the OCR function which is required to recognize C out of the combined image.
Finally, in D3 module, the blowfish algorithm decrypts C and recovers back the secret message m. As illustrated in figure 7, the decryption procedure is summarized in the following steps:

C. Application:
The main concern of the proposed model is providing more security to a highly classified data. It was implemented for public safety applications such as severs in which citizens" personal information for example, full names, addresses, and fingerprints are stored. The proposed model helps to securely transmit or store such information. The recommended configuration of the algorithm is illustrated in figure 8:

Fig 8: The recommended Configuration
The dealer on client machine encrypts the secret information using the encryption module of the proposed model. The information is encrypted using both blowfish then visual cryptography and finally embedded into two cover images. The output Stegoimages STEG1 and STEG2 will be sent to two different storage servers S1 and S2 through point to point wireless links µ1 and µ2 at two different areas A and B respectively. ISSN: 2231-5381 http://www.ijettjournal.org Page 153 The proposed model helps in the case of stealing or breaking into servers S1 or S2 the attacker will not be able to disclose the encrypted stored images.

IV. RESULTS AND PERFORMANCE ANALYSIS
The proposed model was implemented in MATLAB R2016b framework. As an input to the encryption phase, a text message m, "secret", was entered.
Table-1 summarizes the results corresponding to each module E1, E2 and E3 respectively: For the proposed model, the PSNR range starts from 61.94 dB corresponding to a message size of 11958 bytes and reducing as the message size increased. Also, the value of the MSE starts with 0.041584 and increased as the size of the messages increased as shown in figures 9 (a) and (b).
(a) PSNR values using different secret messages.
(b) MSE values using different secret messages. The robustness of the proposed model was examined using statistical cryptanalysis and visual attacks. Statistical cryptanalysis was mounted using StegExpose tool [16] which is used to detect LSB embedded images. The visual attack was mounted through 8-Bitplane analysis of both cover and Stegoimages [17]. The results have shown that the proposed model is resistant to these attacks.

V. CONCLUSION
Both cryptography and steganography have limitations and could be compromised if they were applied individually. By combining them together, a strong algorithm is produced. We proposed an algorithm based on the hybrid approach through double encrypting secret data with blowfish and (2, 2) visual cryptographic then ends with LSB embedding algorithms. The produced Stego-images have perfect quality in terms of MSE and PSNR values and image histogram. The results also have shown that, the proposed model is robust to both statistical and visual steganalysis attacks. The proposed model could be applied in the public security sector to transmit or store private data.