dataesr/scientific-paragraphs-categorization huggingface dataset:
We propose an approach based on Swarm Intelligence -more specifically on Ant Colony Optimization (ACO) -to improve search engines' performance and reduce information overload by exploiting collective users ' behavior. We designed and developed three dif-ferent algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms -NaïveR -ank, RandomRank , and SessionRank -leverage on different principles of ACO in order to exploit users ' interactions and provide them with more relevant results. We designed an evaluation experiment employing two widely used standard datasets of query -click logs issued to two major Web search engines. The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users' intent.
As recently demonstrated  ( Yue, Han, He, & Jiang, 2014 ) , such studies primarily tested implicit collaborative information-seeking systems using simulated query formulation instead of employing user analysis involving human participants. In our research, we employed a classic approach by using two existing datasets to simulate queries to evaluate our system in a real setting. Hence, we deal with the problem of improving search engines' performance by exploiting the actions performed by users. In fact, search engines are tools designed to help people solve their own informational needs and significant room exists for improvements. Queries submitted to search engines can be clustered into three main categories on the basis of users' aim  ( Broder, 2002 ) : Informational queries are issued by users willing to acquire information that they assume is present on one or more Web pages; Navigational queries are being used to get to a particular Web page belonging to an organization or an individual; and, Transactional queries are issued to perform activities using the Web, such as booking a trip or downloading a file.
Learning to Rank Learning to Rank aims at automatically learning the right ranking function from a training set, typically a click-through dataset. Joachims (2002)  outlined three major key points: (1) explicit feedback provided by users cannot be taken for granted and, as a matter of fact, is unnecessary because the information given by the query-click logs are enough; (2) in fact, they can be used as a measure of relevance (with a relative scale); and (3) a machine learning method -Joachims used a Support Vector Machine (SVM) -can be used to obtain a new ranking function that improves search engine performance. In addition to SVMs, other machine learning algorithms may be used, such as RankBoost  ( Freund, Iyer, Schapire, & Singer, 2004 ) , RankNet  ( Burges et al. , 2005 ) , QBRank  ( Zheng, Chen, Sun, & Zha, 2007a ) , GBRank  ( Zheng et al.
Consequently, in the following experiments, we employed query-click log datasets provided by two famous search engines -AOL  ( Pass, Chowdhury, & Torgeson, 2006 )  and Yahoo! -to carry out experiments on the proposed algorithms (further details on these datasets can be found in the Appendix); we have validated the new ranking produced by each algorithm using the very same datasets clustered by each user's search session, applying a simple temporal threshold (30 min, as suggested by several previous studies  ( Fox et al. , 2005; Gao, Yuan, Li, Deng, & Nie, 2009 ) ) to decide whether two actions performed by a single user belong to the same search session. Hence, we considered two interactions as belonging to the same search session when they were both (1) issued by the same user, (2)contained the same query, and (3) performed within 30 min.
After selecting the two datasets, we needed a performance measure to evaluate all the different algorithms we propose. Sakai (2007)  compared different performance measures that take into account the documents' positions and recommended the so-called Normalized Discounted Cumulative Gain (NDCG) for its simplicity and robustness  ( Jarvelin & Kekalainen, 2002 ). The NDCG consists of a parameter and two functions: 1. k ∈ N 0 is a cutoff parameter defining the number of elements in the results list to be considered; 2. the gain function measures the benefit earned by the user (if a document is only relevant or irrelevant, then the gain is binary), whereas 3. the discount function weighs the documents' relevance on the basis of the position in the results list. Moreover, to obtain an absolute measure -in the real interval [0,1] -we normalized it with respect to the maximum obtainable gain.
Few publicly available datasets already provide explicit relevance judgements for each document they refer: the Yahoo! dataset is one of them, but the AOL one does not; as we state before though, user clicks can be used as a relative measure of the perceived relevance by each user. In this case, we then build the array y by assigning 1 to each document selected by the user in the search session we are currently trying to compute the NDCG for, 0 for the ones that were not selected. To summarise, computing NDCG scores for each search session contained in the datasets is possible using the available relevance judgments for Yahoo! and by assuming that the visited results are the relevant ones for AOL. Effectively, by doing so we are actually evaluating the current performance of the two search engines, which will be the baseline we are comparing our algorithms against.
Evaluation of Ant Colony Ranking algorithms We described hitherto how search engine performance are evaluated by employing the query-click log containing users' interactions. As we previously stated, the proposed algorithms require queries, clicks, and search sessions to adjust the pheromone deposited on each document in relation to any query submitted to the search engine. The chosen query-click logs contain all this information and, thus, can be employed to simulate our algorithms in a real world scenario. The validation strategy is dictated by the constraints over our context: since we are using these datasets to simulate real users' interactions, we have to obey to time constraints; our test set will then always be consequent to the training set, since the system can only be trained using past interactions, forbidding any kind of cross-validation.
Furthermore, because we sought to evaluate how the algorithms parameters affect performance, we tested three different values of δ and of the evaporation time (one hour, one day, and one week), and three session duration values for the SessionRank training (one, five, and 25 min, namely very brief, average -according to Xu and Mease (  2009 ) -and longduration search sessions); besides, since RandomRank is a probabilistic algorithm, each experiment were repeated 5 times using different seeds of the random number generator. Thus, the evaluation involved 162 different experiments. We carried them out using a t2. small Amazon EC2 instance running Amazon Linux AMI and the DEX library to manipulate both datasets  ( Martínez-Bazan et al. , 2007 ) ; each run took about 3 h to complete. To summarize, our evaluation's aim was to measure and compare the proposed ranking algorithms' performance using data provided by two famous search engines.
Also, the NDCG values computed respectively on the Yahoo! and AOL datasets by maintaining the default search engine's ranking represent our benchmark. As expected, NaïveRank performs better on the larger dataset (i. e. Yahoo! in Table  2  ); this seems reasonable, since it follows from the Ant Colony Optimization (ACO) approach: the more data is recorded about users' behavior the better the algorithm will perform. More surprising are the differences obtained with the same algorithm using different pheromone evaporation intervals and by halving ( δ) times. Normally, one would expect that depending on the halving delta factor timeframe the algorithm would perform better in adapting to users' behavioral changes: in fact, no matter which dataset we used, we obtained the best performance by setting δ = 7 d (a week timeframe); this confirms what implied by Liu, Jones,   and Klinkner (2006) about the weekly cycle of the majority of queries which states that users will perceive search engine results as relevant and up to date generally only during one week after which they would expect the results to be updated.
While the evaporation time is important to achieve good performance, using a δ set to the weekly cycle of queries allowed us to arbitrarily choose the evaporation time, significantly reducing the computational costs. Considering RandomRank performance, it is interesting to notice how it differentiates from NaïveRank: the variations take place mostly for NDCG@1 (i. e. the score related only to the first displayed result), while for NDCG@3 and NDCG@10 results are almost identical to NaïveRank. This is due to the probabilistic nature of the random ranking; indeed, probability has on average an higher effect when smaller set of documents are considered due to probability of selecting more than one element of the set for NDCG@3 and NDCG@10. A slightly more interesting result comes from analyzing how such variations actually occur: when tested against the AOL dataset -the smaller one -RandomRank underperforms NaïveRank by about 10%.
For Yahoo! Dataset, performances are almost the same for both the algorithms. The exception is the configuration; with δ = 1 h (i. e. the halving factor) and a weekly evaporation cycle. Then RandomRank doubles the score obtained with the same configuration by NaïveRank. This could confirm once more what we stated previously when discussing NaïveRank's results about the weekly cycle of search queries, as introduced by  Liu et al. (2006)  : the introduction of probability helps users discovering new interesting documents among results. In this particular case the pheromone updates on weekly basis according to users' perception of documents relevance. Finally, the results related to the SessionRank algorithm show the different timeframes used by the algorithm to identify a user's search session in its training phase.
One can notice straightaway that the training sessions duration is mostly irrelevant; this may be caused by the average short length of search sessions, as demonstrated in  Silverstein, Marais, Henzinger and Moricz (1999)  , since users tend to perform brief sessions, performing different queries; thus search sessions will have only few clicks performed in a short time span and the amount of pheromone to be deposited by the algorithm will be rather fixed, causing no effect. Also, the algorithm performs worse than NaïveRank using the first dataset, while outperforming it with the second one; this is still due to the variation in training set size: a greater number of search sessions used in training causes an improvement due to the greater quantity of pheromone available to be deposited by each user; thus, the pheromone's modulation -inspired by the ACO metaheuristic -improves the algorithm performance proportionally to the number of interactions recorded during the training phase.
Fig. 1  recaps our results: the two plots represent only the best performance obtained by the different configurations of our algorithms -showed on the x-axis -with the two datasets used; on the y-axis we show the different cutoff points used to compute the related NDCG measure, and the size of the points show the actual NDCG value we obtained, bigger for large values. Finally, the green color is used to show a NDCG measure which improves over the baseline ranking algorithm applied by the search engine, red if otherwise. Summarizing these findings, we argue that our first two proposed algorithms could be employed in improving results ranking produced by two particular sets of queries: being a simple application of the ACO technique to Web pages ranking, NaïveRank works well for embedding a plain concept of popularity into the ranking measure.
We noticed three types of such problematic sessions in our datasets: atypical Web search sessions  ( Eickhoff, Collins-Thompson, Bennett, & Dumais, 2013 )  are being produced by users with atypical information needs, i. e. those outside their regular areas of expertise (often triggered by external events, such as pending medical treatments, financial deadlines or upcoming vacations); exploring sessions  ( Awadallah, White, Dumais, & Wang, 2014 )  are those where users are engaged in an open-ended and multi-faceted information-seeking task to foster learning and discovery; struggling sessions  ( Awadallah et al. , 2014 )  are those where users are experiencing difficulty locating the required information. Examples of both exploring and struggling sessions can be found in Fig. 2. Given that SessionRank exploits not just single interactions with the search engine but whole search sessions, it seems reasonable to argue that the ranking of results related to the aforementioned search sessions could benefit from the introduction of our last ACO-inspired algorithm.
Thus it could be useful to test our algorithm against a different dataset containing long-lasting search sessions of this type. Alternatively, one could use some query-similarity measure with the same datasets we employed, in order to cluster similar queries belonging to the same session. Our findings are summarized in Fig. 3  : it shows both dimensions that our algorithms operate on: search sessions' length on the x-axis and documents update frequency on the y-axis. The horizontal stripes represent the aforementioned examples of documents sets to be ranked, such as breaking news, encyclopedia definitions and catalog's products, while the vertical ones are the three kinds of search sessions outlined in the previous paragraph  ( Awadallah et al. , 2014; Eickhoff et al. , 2013 ). The dashed blocks indicate which algorithm we think could be the most effective in ranking results generated for each case.
The NaïveRank seems particularly interesting for informational queries that seek to retrieve results on relatively static information on the Web, such as looking for products in a catalog or encyclopedia entries. RandomRank proved effective for the inverse situation, such as breaking news or a sports event. The SessionRank algorithm was suited for struggling and explorative sessions (in other words, open-ended information-seeking tasks fostering users' learning) or atypical query sessions (generated by external events such as specific treatments, deadlines, or upcoming holidays). We evaluated the three algorithms by designing an evaluation, where we compared the performance of the three proposed ranking algorithms with the data provided by two famous search engines: Yahoo! and AOL. Because our methods are based on users' interactions to discover the most promising results, the datasets were partitioned into a training set and a test set.
We plan to run an online experiment with a wide sample of participants and test the three algorithms in a real time scenario with users in the future. We hope to prove that in an online environment, real time relevant results can also be obtained by users employing an implicit collaborative approach for information seeking and by selecting the right algorithm depending on the types of queries. We also plan on further investigate how blend some concepts explored by existing ACO extensions in our model, such as limiting the amount of deposited pheromone to avoid deposited as in Max-Min Ant System(MMAS) ( Stützle & Hoos, 20 0 0 ). 11 below the last result (next page, footer advert, etc. ), s new query, o other clicks. As for the previous dataset, we employed in our experiments the subset of records related to the queries performed on average once a day during the reference timespan and for which we have the relevance judgments.
Acknowledgments This work has been partially supported by ANTASTIC -a bio-inspired approach to social search interactions. YGGDRASIL: the  Research Council of Norway Grants  for highly qualified, younger researchers (2013). Project n. 220050/F11. We would like to acknowledge the  Research Visibility Award  funded by  Brunel University London  that allowed the main author to establish a research network on collaborative information seeking with the co-authors of this work.
This result led us to conduct further shotgun sequencing of this individual for a total of B69 million, paired-end 75 bp reads and 43 million after quality controls (Methods section). Validation of the iterative mapping approach using MITObim. The lack of a suitable reference mitochondrial sequence necessitated an iterative mapping approach. We used the iterative mapping software package MITObim  17  , which has been used successfully for mitochondrial reconstructions using modern DNA. Reference sequences for the following four species (with GenBank accession numbers) were selected  17  : guanaco (Lama guanicoe, NC_011822. 1), rhinoceros (Ceratotherium simum, Y07726. 1), horse (Equus caballus, HQ439492. 1) and tapir (Tapirus indicus, KJ417810. 1). MITObim reconstructions using the default MITObim mismatch value (15%) and consensus calling method resulted in complete mitochondrial sequences being recovered for each bait reference used, but many discrepancies were evident among the consensus sequences (Supplementary Table  3 ).
5 U ml À 1 ) and 57 ml water followed by a selected number of PCR cycles, involving denaturation for 15 s at 95 °C, annealing for 30 s at 60 °C and primer extension for 1 min at 68 °C. Amplified libraries were cleaned up using a Minelute PCR purification kit following the manufacturer's protocol. Sequencing of library and extraction blanks were included to check for the presence of contamination. Test sequencing and analysis. For each library, we sequenced B2-20 million 75 bp PE read pairs on an Illumina Nextseq 500 sequencing platform. Raw Illumina intensity data were demultiplexed and converted to nucleotide sequences using the Illumina software, bcl2fastq. Adaptor sequences were trimmed from the raw reads, reads with lengths of 30 bp or less were discarded using Cutadapt 1. 4 (ref. 36).
8, a wrapper script for the Mira v4. 0. 2 (ref. 42)  assembler. Direct reconstruction without prior mapping assembly with default parameters was implemented. We initially tested MITObim with the default mismatch value and using four different reference bait mitochondrial sequences, three from the order Perissodactyla and one from the order Artiodactyla. Mira output maf files were then converted to sam files and visualized using Geneious v9. 0. 5 (ref. 43). Visual inspection revealed that MITObim assemblies generated using default parameters contained very large numbers of spuriously mapped reads or misassemblies, and that these regions were typically associated with either very high or very low coverage. MITObim validation. To better evaluate and optimize the MITObim assembly, we tested the ability of MITObim to reconstruct the correct mitochondrial sequence of a Late Pleistocene cave hyena (Crocuta crocuta spelaea), an extinct taxon for which published mitogenome sequences are available that can be used to validate the MITObim assembly.
These validation tests used cave hyena shotgun sequencing data with a similar predicted endogenous content and similar number of reads as MAC002. We iteratively mapped these reads to both the dog (Canis lupus familiaris GenBank accession: NC002008) and the brown bear (Ursus arctos, GenBank HQ685964) mitochondrial genomes, as they are thought to have diverged at a similar phylogenetic time (B50 million years) from Crocuta as Macrauchenia from the Perissodactyla reference sequences used. We implemented mismatch values ranging from 0 to 12%. Output consensus sequences were then compared to one generated from the same raw reads mapped to a cave hyena mitochondrial genome (Genbank, NC_020670. 1) using BWA 0. 7. 8 (Supplementary Table  4 ). MAC002 mitochondrial genome reconstruction. We generated assemblies of the MAC002 mitochondrial genome using mismatch values of 0-6% in steps of 1% for each of the four reference sequences described above, resulting in a total of 28 assemblies.
Protein coding genes were manually identified based on the horse mitochondrial sequence, extracted and translated into their respective amino acid sequences using MEGA6 to check for premature stop codons. We then calculated pairwise distances for the MAC002 sequence from the four references used for assembly, as well as the human mitochondrial sequence (Genbank accession J01415. 2), along a sliding window. Following alignment, sites with missing data were removed manually in MEGA6 and pairwise distances calculated for windows of 500 bp at 50 bp intervals using a custom perl script. Finally, the trimmed and merged MAC002 reads were mapped back to our final consensus sequence using BWA  38  and parsed using samtools  39  , allowing investigation of aDNA damage patterns and read length distributions using mapdamage2.
0. 2-8 (ref. 21; Supplementary Figs  2  and 3 ). Retrospective mapping of other Macrauchenia and Toxodon samples. We retrospectively mapped reads from the other analysed samples to our final Macrauchenia consensus sequence using BWA 0. 7. 8 (ref. 38), to better assess the presence of endogenous mitochondrial fragments in these samples. Phylogenetic reconstruction. Our final Macrauchenia consensus sequence was aligned with 94 other mitochondrial sequences, including representatives from all major clades of the Laurasiatheria superorder (Supplementary Table  10 ). All sites containing missing data (N) were removed from the alignment manually, along with the control region, resulting in 12,997 bp of aligned sequence. tRNA and gene positions within this alignment were determined manually for later partitioning analyses.
Acknowledgements We thank members of the following institutions for permission to sample specimens in their care: in Argentina, the  Museo Argentino de Ciencias Naturales 'Bernardino Rivadavia' (Buenos Aires), Museo de La Plata, Museo Municipal de Ciencias Naturales 'Lorenzo Scaglia' (Mar del Plata)  and  Museo Paleontologico de San Pedro 'Fray Manuel de Torres' (San Pedro) ; in Chile, the  Departamento de Antropologı ´a (Universidad de Chile)  and the  Facultad de Patrimonio Cultural y Educacion (Universidad SEK Chile) ; in  Uruguay ,  Museo Nacional de Historia Natural (Montevideo) ; and in France,  Museum national d'Histoire naturelle (Paris). All fossil samples were collected before 2010. We also thank  Rheon Slade  for assisting in the modifications of some figures. This work was partly supported by  National Science Foundation   DEB 1547414  (R.
Results and Discussion Detection and segmentation of the iris in digital imagery. Prior to colour assessment, the iris needed to be segmented from the pupil and sclera. Several approaches have been proposed previously for the segmentation of the iris in digital eye imagery  [28] [29] [30] [31] [32]. The utility of a certain approach depends strongly on the properties of the given image data. In the majority of imagery available to us, (i) the pupil was always centred in the middle (Fig. 1a ), (ii) the iris was fully visible (those images where it was not were excluded from the analyses), and iii) eye lashes rarely overlapped with the iris. Because of these features, we followed a previously proposed 30 two-step procedure, which we implemented in Matlab (R2007a). First, we used a Canny filter  33  to distinguish the rim confining the iris and then applied the Hough transformation  34  to detect the iris circle.