dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of dynamic obstacles in the environments was negligible, so that ground-truth laser-based localization would not be affected. The camera's view of the ceiling was not obstructed by people or other dynamic obstacles. This scenario was performed in environments A and B. The accuracy of the localization in this scenario demonstrates the viability of our approach. Scenario 2 consisted in sampling the data from environment A while changing the artificial lighting conditions. During this scenario, we used lighting configurations where a subset of the lights was switched on. The training dataset for the CNNs did not contain this lighting configuration. While the accuracy of the localization is worse than with all lights on or off, our approach manages to produce a relatively stable localization.
DISCUSSION As with any supervised learning approach, we are highly dependent on the quality of the dataset. Acquiring ground truth data for the CNNs proved difficult, due to inaccuracies in the laser-based localization. There would be room for improvement if a more reliable ground truth method were used. In order to deploy a robot using our method, one still needs to spend a fair amount of time acquiring the dataset and ensuring coverage of the whole environment, although this process could be automated with an environment exploration algorithm. Acquiring ground-truth supposes that one has access to an alternative method for localization, potentially requiring more expensive sensors. However, once the dataset has been acquired by a single robot, our method can be deployed on any number of robots using a simple inexpensive camera.
Further experiments would need to be conducted in truly crowded scenarios to confirm this. Table  2  shows average position and orientation errors from recent 6-DoF camera relocalization works using the 7-scenes dataset where environments have an average size of 13 m 3. While our work solves for the 3-DoF case, our test scenarios cover much larger environments, often with fewer distinctive features. Although the applications of our approach (3-DoF indoor robot localization) are different that the ones in the related works (6-DoF camera relocalization) we find it beneficial to provide such a comparison.
FUTURE WORK In order to better deal with crowded environments, a training dataset could be acquired when people are present in the environment. Such a dataset would potentially be harder to acquire; however, the network should be able to provide better localization than when trained on a dataset of a static environment. The existing dataset could be modified by adding people at random locations in the images. In order to incorporate this work into a complete localization framework, we can filter the output of the pose prediction networks to provide a more stable estimate. One could also consider a set of poses over time in order to discard outliers. The stable pose estimation can be combined with other localization data such as odometry using sensor fusion. The speed of the pose update could be improved by adopting a more elaborate CNN architecture.
Metagenomics for diagnostics pipeline We developed and tested a metagenomics pipeline for Xf detection and subspecies identification (Fig. 1 ). We tested this pipeline based on three types of DNA samples: from bacterial colonies in culture, spiked plant samples, and naturally infected plant samples (Fig. 1B ). To recover and identify Xf subspecies and compare it to the already sequenced genomes, we developed a pipeline that uses six different tools and custommade databases  (22)  (Fig. 1C ). The pipeline recovers Xf reads with the software Kraken2 and a custom-made database  (22). The database has user-specified genomes for Xf reads identification. The user-specified genomes belonged to Xylella (n=81), Xanthomonas sp. (n=10), E. coli (n=1) and several plant sequences from NCBI (Table  S1 ).
The database had plant sequences because some Xf genomes from NCBI contained plant genomic DNA sequences. We could not clean all 18S sequences, plant plastids, or chloroplast reads from the NCBI Xf genomes. Therefore, the plant reads in the database serve as a filter to ensure plant reads were not misidentifying as Xf reads. After Kraken2, the pipeline de-novo assembled the recovered Xf reads into contigs with the program SPAdes  (23). The pipeline used the Xf contigs for four different analyses: 1) subspecies identification, 2) phylogeny reconstruction, 3) identification of the already sequenced genetically closest strains, and 4) alleles for MLST profile and virulencerelated genes determination. The pipeline used Xf contigs, and the tool SendSketch from the BBMap software to identify subspecies.
Then it used Pyani and LINbase software to reconstruct phylogeny by ANI  (24, 25). Next, it assigned Xf strains to each Xf contig to identify the closest strains with the tool BBSplit from BBMap software. Finally, to identify specific genes or alleles, the pipeline used local BLAST with two type of subjects: a) one subject was the reported MLST allele genes and b) the other subject was the protein sequences from genes associated with virulence.
To test this, we created a smaller Kraken2 database with 30 genomes instead of 81. These 30 Xf genomes had complete assemblies. We recovered 1 to 2% fewer Xf reads with the new database, and the subspecies distribution remained the same (data not shown). The results indicated that Xf subspecies classification is not related to the level of genome assembly. Therefore, the original Kraken2 database with 81 NCBI Xf genomes was retained for all further analyses. For the second analysis, we manually assessed the SendSketch sensitivity to mixed infections with new in-silico samples. The new samples included a set amount of barley reads in silico spiked with variable amounts of Xff CFBP 7970 and Xfm CFBP8418 reads (Table  S2 ). For these new in-silico samples, we recovered Xf reads with Kraken2, assembled the reads as contigs and run SendSketch to identify subspecies.
43 will be Xfm single infection (Fig. S2 ). To evaluate the pipeline with samples free of Xf, we used extracted DNAs of two healthy plant samples and a non-Xylella controls (i. e. , barley leaves infiltrated with Xanthomonas). For the artificially inoculate barley samples, Kraken2 software recovered 20 to 30% of the total reads as Xanthomonas. For all four Xf-free samples, Kraken2 recovered six to 19 Xf reads (Table  S2 ). All these Xf reads corresponded to plant reads based on the BLAST webtool from NCBI. Based on these results, the pipeline considers a sample Xf-free when it cannot recover more than 19 Xf reads (Fig. S3 ).
The ANI values were consistent with the Xf contig abundance (Fig. 2 , Table  S2 ). All six Italian samples and CFBP 8418 iSeq100 had 99 to 100% identity to Xfm and less than 97% identity to Xff and Xfp. The control sample, CFBP 7970 iseq100, had 100% identity to Xff and less than 98% identity to Xfm and Xfp (Fig. 2 , Table  S2 ). For strain identification, we used the program BBSplit and the Harvest suite. For each sample, we selected the top three closest strains based on the program BBSplit output. Then, we used these closest strains and the sample to compare the number of single nucleotide polymorphisms (SNPs) with the Harvest suite. For the sample CFBP 8418 iSeq100, the closest strain with 30 SNPs was Xfm CFBP 8418 (Table  S5 , S6). For the sample CFBP 7970 iSeq100, the closest strain with fewer SNPs was Xff CFBP 7970.
We decided to add plant reads in the database to filter out potential plant contamination. We still recovered Xf reads for some plant samples reported as Xf-negative. Therefore, we manually examined the contigs of samples with less than 30 Xf contigs to determine if they have a low Xf concentration or are negative samples. More than the Cq values, the contig evaluation will be necessary to determine if a sample is truly Xf -negative. We observed that the Kraken2 tool not only recovered Xf reads but also identified Xf subspecies. We decided not to use Kraken2 to identify subspecies because we found that the recovery is affected by incomplete NCBI Xf genomes subspecies information. Kraken2 uses by default the NCBI taxonomy to classify reads, if the genomes used to build the database does not have a subspecies information it will keep most of the reads at the species level.
Metagenomics sequencing is becoming a more affordable and faster approach for diagnostics. For example, the whole detection/identification with qPCR and MLST scheme could have an estimated cost of 52-54USD per sample and takes three to four days to detect one to seven genes. With the iSeq100, it could cost 50-70USD (when having 12 samples in the same run) and take two days but while also allowing to get a complete genomic analysis of the plant and its pathogenic and commensal microbiota. In conclusion, our pipeline provides Xf taxonomy and functional information for diagnostics without extensive knowledge of the host or pathogen. The pipeline databases used for the analysis can be public repositories or privately collected gDNA and could be adapted by the user and tailored to different plant pathogens.
Reactions were carried out after boiling bacterial suspension for 10 min. The DNA of one isolate among those that tested positive by qPCR from each plant, was extracted using the CTAB based protocol and further characterized to subspecies and ST level following the Multi Locus Sequence Typing (MLST) approach  (34). The GoTaq probe qPCR Master Mix (Promega, A6102) and GoTaq G2 (Promega, M784B) polymerase were used for qPCR and conventional PCR experiments, respectively. The bacterial DNA of three isolates from R. alaternus (IT1-Ra to IT3-Ra), one from S. junceum (IT1-Sj), one from P. myrtifolia (IT1-Pm) and one from Prunus dulcis (IT1-Pd), were sequenced in this study. The assembled genomes of the six isolates were deposited on NCBI under the Bioproject PRJNA728043. Approximately 50-100 mg of grape leaf or petiole tissue was excised from each sample using a razor blade.
Then, the libraries were diluted to have the same starting concentration prior to sample pooling. Eight to 12 libraries were mixed together, and 1 nM of pooled library was used per run. The sequencing settings were paired-ended (PE) read type, 151 read cycles and eight index cycles. In the iSeq 100 System, the illumina GenerateFASTQ Analysis Module for base calling and demultiplexing was selected. After sequencing for 17 h, fastq paired end read files were extracted from the machine for subsequent analysis.
Pipeline for Xylella sp. detection, classification and quantification via metagenomic analysis Fastq files and the program Kraken2 were used to recover Xylella reads  (22). The Kraken2 command options were: --paired, --minimum-hit-groups 5, --report and -db. The database was created with 92 NCBI genomes: 79 from Xf, two from Xylella taiwanensis, ten from Xanthomonas sp. and one from Escherichia coli (Table  S1 ). The 81 Xylella genomes were used to recover Xylella reads. The last 11 genomes were added to remove reads common to Proteobacteria that might give a false positive match. The tool SendSketch with nt server was used to make sure the 81 NCBI Xf genomes were not contaminated with plant reads (BBMap -Bushnell B. sourceforge. net/projects/bbmap/; October 30, 2019). Forty-nine NCBI sequences from plant 18S and chloroplast were added to the Kraken2 customized database to avoid extracting reads annotated as plant reads (Table  S1 ).
The reads classified as Xylella were extracted with the script extract_kraken_reads from the KrakenTools suite (GitHub jenniferlu717/KrakenTools). The extracted Xf reads were used for downstream analysis. First Xf reads were de-novo assembled with the software SPAdes (23) using defaults settings and the option --only-assembler. Second, the Xf contigs were the query sequences in the Basic Local Alignment Search Tool website (NCBI) to confirm if they were Xf reads, or misclassified plant reads. The Blastn parameters were Nucleotide collection (nr/nt) as database and megablast program selection. The Xf-positive contigs were used in four different analyses: 1) to identify subspecies, 2) to reconstruct phylogeny, 3) to identify the genetically closest strains already sequenced and 4) to identify alleles from specific genes and the MLST profile.
To identify subspecies, the tool SendSketch was run with the parameters, mode=sequence, records=2, and format=3, minani=100, minhit=1, address=ref, level=0 (BBMap -Bushnell B. sourceforge. net/projects/bbmap/). Only contigs with 100% average nucleotide identity (ANI) were used to identify Xf subspecies. The Xf contigs with no hits were considered as core sequences. For visualization, results were plotted using stacked bars. To reconstruct phylogeny, ANI was calculated using the software Pyani (v. 0. 2. 10) and LINbase  (24, 25). For Pyani, the option -ANIm was set and for LINbase the "Identify using a gene sequence" was set as identification method. The R package ComplexHeatmap was used to visualize the ANI cluster analysis with the parameters clustering_distance_rows = robust_dist and clustering_method_rows = "average".
Robust_dist was a function suggested by the ComplexHeatmap tutorial. To identify the genetically closest already sequenced Xf strains, the tool BBSplit was run with Xf-contigs (BBMap -Bushnell B. sourceforge. net/projects/bbmap/). The tool used 81 Xf genomes from NCBI and the options minratio=1 ambig=best. For comparisons, each sample was normalized to their total Xf contigs and plotted using the R package ComplexHeatmap. For the isolated bacterial genomes, the most abundant strains were used as reference to identify genomics variants using Harvest suite tools  (35). To determine specific gene alleles, percentage of identity was calculated using Xf contigs as query and the Blastn algorithm (Nucleotide-Nucleotide BLAST 2. 8. 1+). The database contained complete nucleotide sequences for all alleles for the seven genes used for ST identification (cysG, gltT, leuA, malF, nuoL, holC and petC)  (34).
All 147 alleles were downloaded from the website PubMLST (36) (Last updated: 2019-03-06). To determine the presence of reported Xf virulence-related or common to several plant pathogenic bacterial genes  (37, 38) , percentage of similarity was calculated using Xf contigs as query using Blastx algorithm (Nucleotide-Nucleotide BLAST 2. 8. 1+. The databases contained complete amino acid sequences for gumBCDE, pilBMQTVW, rpfCEFG, tolC, 6-phosphogluconolactonase (pgl), and hemagglutinin from the Xfm M12 and Xff M23 NCBI genomes.
MiSeq sequencing To validate the iSeq 100 results, the same iSeq 100 libraries were used for MiSeq deep sequencing. Nine samples were selected, at least one from each iSeq 100 run, making sure not to use the same i5 and i7 tags. These nine libraries were sent to the Animal Disease Diagnostic Laboratory (Ohio Department of Agriculture, Reynoldsburg, Ohio) for sequencing. Library preparation was performed using an Illumina DNA Flex kit, and 2x250 sequencing was performed on the MiSeq platform using V3 chemistry. The pipeline described above was used to analyze the MiSeq fastq files. fastq reads from all samples were then used for metagenomic analysis. We created a database to recover Xf reads using Kraken2. The database contained Xylella, Xanthomonas and Escherichia coli genomes. We also added plant plastid genomes to remove false positive results.
ACKNOWLEDGEMENTS The authors are grateful for funding support from the  Ohio Department of Agriculture Specialty Crops Block Grant  ( AGR-SCG-19-03 ) and  USDA NIFA FACT  ( 2021-67021-34343 ) to JMJ. Sara Campigli  was financed by a grant from the  Phytosanitary Service of the Tuscany Region (Italy). We would like to thank  The Ohio Supercomputer Center  for providing High Performance Computing resources. We would also like to thank  Dr. Stephen Cohen ,  Dr. Jeff Chang , and  Dr. Alexandra Weinsberg  for their valuable input for the experiment development and editing.
The difficulty lies in the fact that Mobile Application Development (MAD) is a sophisticated task that presents a series of challenges and requires that decisions be made throughout the development lifecycle until the point of deployment  [Beyer, 2015]. However, the current development approaches used to support front and back-end tools for MAD are inadequately aligned with multi-experience requirements  [Jason Wong, 2018]. The key challenges to MAD include: (1) Different mobile operating systems: the heterogeneity of specifications for the current mobile operating systems (IOS, Android, Windows Mobile, etc. ) complicates the process of developing a consistent application that takes into account the hardware and software requirements of each mobile device with a different mobile operating system.
Katirtzis et al. in [Katirtzis et al. , 2018]  presented MAPO (Mining API usage Pattern from Open source repositories) system for processing source files and clustering the included API methods which are analysed to infer ranked list of API usage patterns by finding similarities with the developer context. Another attempt is MUSE  [Moreno et al. , 2015]  which recommends to the developer certain code derived from examining source codes and cluster code snippets. Usage Pattern Miner (UP-Miner)  [Wang et al. , 2013]  was designed to automatically mine the usage patterns of API methods from various source codes. Experiments conducted using a Microsoft codebase dataset to evaluate Up-Miner have proven its effectiveness and outweighed baseline approaches using certain metrics. Saied et al. in [Saied et al.
, 2015]  presented a Multi-Level API Usage Patterns (MLUP) system as an approach for quarrying and inferring the co-usage relationships between various methods of the API of interest across interfering usage scenarios. They incorporate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering technique to assemble API methods that are commonly used coherently in software development projects. RSs for software testing are also proposed; SoTesTeR  [Ibarra and Rodriguez, 2019]  is a platform for recommending testing and quality assurance techniques using a content-based approach. Yadav and Dutta in  [Yadav and Dutta, 2019]  introduced a prioritising mechanism for software testing using a test cases clustering technique to minimise the time and effort required to conduct regression testing for software maintenance activities.
Recently,  Silva et al. in [Silva et al. , 2020]  explored patterns in the development of Intelligent Software Engineering (ISE) solutions. To do so, they analysed 42 papers, using a thematic analysis approach to understand how previous researchers had reused knowledge and applied it to solve a SE problem. They found that researchers use internal and external knowledge sources, and rely mostly on structured data to develop ISE solutions. dos  Santos et al. in [dos Santos et al. , 2019]  presented a knowledge-based recommendation system that considers the critical factors of a given project to indicate which multi-platform application development framework is most suitable for that project. However, the scope of the given recommendation is limited to multi-platform application development. Evaluation of the current approaches: Despite the ongoing efforts to tackle obstacles that face software engineers at various stages of the development lifecycle, the sophistication of the mobile app development -as an ecosystem per se -as well as the advances in smartphone devices and various development technologies, open wide the door to future research avenues.
In particular, the current approaches to recommending relevant artifacts that will assist developers in their development journey are inadequate and inferior. The following points indicate the shortcomings of the current approaches and demonstrate the superiority of our proposed system: (i) A cohort of current efforts rely mainly on clustering or statistical techniques that generally ignore the semantics of artifacts  [Katirtzis et al. , 2018; Moreno et al. , 2015; Rubei et al. , 2020; Saied et al. , 2015; Wang et al. , 2013; Yadav and Dutta, 2019]. Incorporating semantic analytics leveraged by a domain-ontology, semantic Web technologies and linked open data can enrich the domain ontology with instances extracted from textual contents of the collected datasets. (ii) Preferencing and profiling dimension of existing approaches is insufficient or less-focussed on the embodied techniques.
The proposed approach aims to implicitly and explicitly detect developers' interests through the construction of a multi-dimensional semantic technique that frame users' preferences based on the designated domain. (iii) Datasets collected to evaluate the current approaches were obtained mainly from few sources  [B. Lin, 2018; Rubei et al. , 2020]. However, the artifacts of interest can exist in heterogenous forms and can be obtained from various resources. Our proposed approach tackles this issue by designing a system that can collect, process and retrieve artifacts obtained from tutorial and Q&A websites as well as online social networks. (iv) The temporal dimension of the current systems is not effectively incorporated. Developers' behaviours, as well as project requirements, change over time; hence, users' profiles and preferences may require constant amendments.
Acquisition and Pre-processing At this stage, heterogeneous types of data are generated from different online repositories including tutorials, API documentations, Q&A repositories, to name a few. Those knowledge bases that allow APIs to access and retrieve their online content will be incorporated in this study. Moreover, data acquisition will include accessing and collecting social data pertaining to the users of the system. Several APIs will be utilized to extract batches of social data in a timely fashion. Various pre-processing techniques are applied after the data acquisition phase: specifically, data cleansing and restructuring. During the data cleansing process, datasets are scrutinised so as to detect and deal with, for instance, corrupted, incorrect, redundant, and/or irrelevant data.
Domain Knowledge Inference This is the "semantic kitchen" where mobile development domain ontology will be constructed to provide formal representation of the designated knowledge by identifying all the related concepts within the domain and the relationships between them. This domain ontology will provide a common shared vocabulary to be used for modelling the domain with all embodied properties and relationships. Moreover, ontologies and various semantic web technologies and repositories are used to infer implicit knowledge from textual content as well as to model and represent the knowledge. In particular, this module will embody the ontology and semantic data interlinking techniques which facilitate the interoperability of information. The interlinking and enrichment process incorporates dissimilar vocabularies and Linked Open Data repositories such as Friend-of-a-Friend (FOAF)  [Brickley and Libby, 2014] , Dublin Core (DC)  [Weibel, 1998] , Simple Knowledge Organization System (SKOS)  [Isaac and Summers, 2009; Miles and Bechhofer, 2009] , Semantically-Interlinked Online Communities (SIOC)  [Breslin, 2005]  will be used to enrich the semantic description of resources obtained from the crawled datasets using an annotation component.
In addition to ontology and vocabulary reuse, interlinking includes the semantic relationship between similar entities stored in other datasets. The main objective of this module is twofold: to build the domain ontology for mobile app development, and to use the incorporated lightweight ontologies to enrich this domain ontology with specific semantic conceptual representations of entities obtained from the collected datasets. The second objective is to assist the process of user profiling and preferencing by means of detecting their domains of knowledge and interest obtained from examining and semantically enriching their social data content. Figure  2  shows a collection of Twitter messages about mobile app development, in particular the MAD methodologies and jobs being offered.