dataesr/scientific-paragraphs-categorization huggingface dataset:
EXPERIMENTAL SETUP 3. 1 Datasets We evaluate GoldFinger on six publicly available datasets (Table  2 ). To apply Jaccard's index, we binarize each dataset by only keeping in a user profile P u those items that user u has rated higher than 3. Movielens. Movielens  [22]  is a group of anonymous datasets containing movie ratings collected on-line between 1995 and 2015 by GroupLens Research  [40]. The datasets (before binarization) contain movie ratings on a 0. 5-5 scale by users who have at least performed 20 ratings. We use 3 versions of the dataset, movielens1M (ml1M), movielens10M (ml10M) and movielens20M (ml20M), containing between 575,281 and 12,195,566 positive ratings (i. e. higher than 3). AmazonMovies. AmazonMovies  [36]  (AM) is a dataset of movie reviews from Amazon collected between 1997 and 2012.
We restrain our study to users with at least 20 ratings (before binarization) to avoid users with not enough data (this problem, the cold start problem, is generally treated separately  [28] ). After binarization, the dataset contains 57,430 users; 171,356 items; and 3,263,050 ratings. [46]  is a dataset of co-authorship from the DBLP computer science bibliography. In this dataset, both the user set and the item set are subsets of the author set. If two authors have published at least one paper together, they are linked, which is expressed in our case by both of them rating each other with a rating of 5. As with AM, we only consider users with at least 20 ratings. The resulting dataset contains 18,889 users, 203,030 items; and 692,752 ratings.
DBLP. DBLP Gowalla. Gowalla  [16]  (GW) is a location-based social network. As DBLP, both the user set and the item set are subsets of the set of the users of the social network. The undirected friendship link from u to v is represented by u rating v with a 5. As previously, only the users with at least 20 ratings are considered. The resulting dataset contains 20,270 users, 135,540 items; and 1,107,467 ratings.
Experimental settings We set k to 30 (the neighborhood size). The parameter δ of Hyrec and NNDescent is set to 0. 001, and their maximum number of iterations to 30. The number of hash functions for LSH is 10. GoldFinger uses 1024 bits long SHFs computed with Jenkins' hash function  [24]. Evaluation metrics. We measure the effect of GoldFinger on Brute Force, Hyrec, NNDescent and LSH along with two main metrics: (i) their computation time (measured from the start of the algorithm, once the dataset has been prepared), and (ii) the quality of the resulting KNN (Sec. 2. 1). When applying GoldFinger to recommendation, we also measure the recall obtained by the recommender. Throughout our experiments, we use a 5-fold cross-validation, and average results on the 5 resulting runs. GoldFinger is orders of magnitude faster than MinHash, whose overhead is prohibitive.
Implementation details and hardware. We have implemented Brute Force, Hyrec, NNDescent and LSH (with and without GoldFinger) in Java 1. 8. Our experiments run on a 64-bit Linux server with two Intel Xeon E5420@2. 50GHz, totaling 8 hardware threads, 32GB of memory, and a HHD of 750GB. Unless stated otherwise, we use all 8 threads. Our code is available online 5.
Random sampling and MinHash Random sampling. When applied to Brute Force on ml10M, a random sampling of 75 items per profile decreases the computation time from 2028s to 1626s, while delivering a quality of 0. 94. By contrast, GoldFinger (with 1024 bits) is substantially faster (producing a graph in 606s, a 62. 7% improvement compared to random sampling) while producing a similar graph (also delivering a quality of 0. 94). Similar results are obtained on other datasets or on Hyrec, (with temporal gains by GoldFinger ranging from 54. 9% to 72. 2% compared to sampling, and producing better graphs across the board, except in one case in which GoldFinger's quality is slightly lower, by 0. 04). Together these results show GoldFinger consistently outperforms random sampling. MinHash. We use b = 4 and 256 permutations for BBHM (a configuration that provides the best trade-off between time and KNN quality).
MinHash decreases the computation time on ml10M from 2028s to 1028s with a Brute Force approach, while delivering a quality of 0. 93. GoldFinger (with 1024 bits) is both substantially faster (producing a graph in 606s, a 41. 1% improvement compared to MinHash), and better (delivering a quality of 0. 94). Similar results are obtained on other datasets, or when using LSH (with an average temporal gain of 34. 8% compared to MinHash, temporal gains ranging from 2. 7% to 58. 8%, and while producing better graphs across the board). When used on AmazonMovies with NNDescent and Hyrec, MinHash causes these two algorithms to collapse, yielding qualities below 0. 11. Together these results show GoldFinger outperforms MinHash by a wide margin. Furthermore, computing MinHash summaries is extremely costly (as it requires creating a large number of permutations on the entire item set), which renders the approach self-defeating in our context.
Brute Force, Hyrec, NNDescent, and LSH The impact of GoldFinger (GolFi) on these four algorithms is summarized in   Figure  7 : KNN quality using a 1024 bits SHF (higher is better). Force on Gowalla) to an improvement of 0. 11 (Hyrec on Amazon-Movies). Excluding LSH on AmazonMovies, DBLP, and Gowalla for the moment, GoldFinger is able to reduce computation time substantially, from 42. 1% (NNDescent on ml1M) to 78. 9% (Brute Force on ml1M), corresponding to speedups of 1. 72 and 4. 74 respectively. Experiments show that the most important parameter is the distribution of ratings, rather than the sparsity or the number of items/users. The more ratings are concentrated onto a small subset of items, reducing collisions, the higher the accuracy provided by GoldFinger. Ratings appear to be more concentrated of few items in movie datasets which explain the differences between the datasets.
GoldFinger only has a limited effect on the execution time of LSH on the AmazonMovies, DBLP and Gowalla datasets. This lack of impact can be explained by the characteristics of LSH and the datasets. LSH must first create user buckets using permutations on the item universe, an operation that is proportional to the number of items. Because AmazonMovies, DBLP and Gowalla are comparatively very sparse (Table  2 ), the buckets created by LSH tend to contain few users. As a result, the overall computation time is dominated by the bucket creation, limiting the impact of GoldFinger. In spite of these results, GoldFinger consistently outperforms native LSH on these datasets, for instance, taking 62s (with Hyrec) instead of 141s with LSH on AmazonMovies (a speedup of ×2. 27), for a comparable quality.
Memory and cache accesses By compacting profiles, GoldFinger reduces the amount of memory needed to process a dataset. To gauge this effect, we use the perf 6 command line tool to profile the memory accesses of GoldFinger. perf uses hardware counters to measure accesses to the cache hierarchy (L1, LLC, and physical memory). To eliminate accesses performed during the dataset preparation, we subtract the values 6 https://perf. wiki. kernel. org/index. php/Main_Page returned by perf when only preparing the dataset from the values obtained on a full execution. Table  5  summarizes the measures obtained on Brute Force, Hyrec, NNDescent and LSH on ml10M, both without (native) and with GoldFinger (GolFi). We only show L1 accesses for space reasons, since LLC and RAM accesses are negligible in comparison.
GoldFinger in action: recommendations We evaluate the applicability of GoldFinger in the context of a concrete application, namely a recommender. Item recommendation is one of the main applications of KNN graphs, and consists in providing every user with a list of items she is likely to rate positively. To do so, we compute for each user u and each item i not known to u that is present in u's KNN neighborhood a score score(u, i), using a weighted average of the ratings given by other users in u's KNN: Figure  8 : Recommendation recall using a 1024 bits SHF (higher is better). GoldFinger's (GolFi) recall loss is negligible. score(u, i) = v ∈ knn(u) r (u, i) × sim(u, v) v ∈ knn(u) sim(u, v). Using the KNN graphs computed in the previous sections, we recommend 30 items to each user in every dataset.
Since we use a 5-fold cross-validation, we use the 1/5 of each dataset not used in an experiment as our testing set, and consider a recommendation successful if the user has produced a positive rating for the recommended item in the testing set. We evaluate the quality recommendation using recall, i. e. the number of successful recommendations divided by the number of positively rated items hidden in the testing set. Figure  8  shows the recall of the recommendation made with the native algorithms and with their GoldFinger counterparts on all datasets. The impact on recall spans from a 4. 16% drop (brute force on AmazonMovies) to a gain of +16. 98% (Hyrec on AmazonMovies) with a gain of +0. 84% on average. These results clearly show that the small drop in KNN quality caused by GoldFinger has close to no impact on the outcome of the recommender, confirming the practical relevance of GoldFinger.
Impact on the execution of the algorithm Figure  10  shows how the overall execution time and the quality of Brute Force and Hyrec evolve when we increase the size of the SHFs. (LSH presents a similar behavior to that of Brute Force, and NNDescent to that of Hyrec. ) As expected, larger SHFs cause Brute Force to take longer to compute, while delivering a better KNN quality (Fig. 10a ). The overall computation time does not exactly follow that of individual similarity computations (Figure  9a ), as the algorithm involves additional bookkeeping work, such as maintaining the KNN graph and iterating over the dataset. The KNN quality of Hyrec shows a similar trend, increasing with the size of SHFs. The computation time of Hyrec presents, however, an unexpected pattern: it first decreases when SHFs grow from 64 to 1024 bits, before increasing again from 1024 to 4096 bits (Figure  10b ).
This difference is due to the different nature of the two approaches. The Brute Force algorithm computes a fixed number of similarities, which is independent of the distribution of similarities between users. By contrast, Hyrec adopts a greedy approach: the number of similarities computed depends on the iterations performed by the algorithm, and these iterations are highly dependent on the  distribution of similarity values between pairs of users (what we have termed the similarity topology of the dataset), a phenomenon we return to in the following section.
RELATED WORK For small datasets, KNNs can be solved efficiently using specialized data structures  [7, 33, 38]. These solutions, unfortunately, do not scale, as computing an exact KNN efficiently remains an open problem. Most practical approaches, therefore, compute an approximation of the KNN graph (ANN), as we do. A first way to accelerate the computation time is to decrease the number of comparisons between users, taking the risk to miss some neighbors. Recursive Lanczos Bisection  [15]  computes an ANN graph using a divide-and-conquer method, while NNDescent  [19]  and Hyrec  [8]  rely on local search, i. e. they assume that a neighbor of a neighbor is likely to be a neighbor, and thus drastically decrease the scan rate, delivering substantial speedups. KIFF  [9]  computes similarities only when users share an item.
KIFF works particularly well on sparse datasets but has more difficulties with denser datasets such as the ones we studied. Locality Sensitive Hashing (LSH)  [23]  allows fast ANN graph computations by hashing users into buckets. The neighbors are selected only between the users of the same buckets. Several hash functions have been introduced, for different metrics  [10, 11, 14]. All of the above works can be combined with our approach-as we have demonstrated in the case of NNDescent, Hyrec, and LSH -and are thus complementary to our contribution. Another strategy to accelerate a KNN graph's construction consists in compacting users' profiles, in order to obtain a fast approximation of the similarity metric. Keeping only a fraction of the profiles speeds-up Jaccard computation  [26]  but the resulting approach is not as fast as GoldFinger.
Minwise hashing  [4, 31]  approximates Jaccard's index by only keeping a small subset of items for each user. It is space efficient but has a prohibitive preprocessing. Cui et al. [18]  use a bit array to represent profiles: each feature has its value rounded to either 0 or 1, and stored in one bit. The approach is not scalable for the dataset we study. Closer to our work, Gorai et al. [21]  use Bloom filters to encode the profiles and then estimates Jaccard's index by using a bitwise AND. Despite providing privacy, the resulting loss in precision is prohibitive. Sketches  [13, 17, 41]  are other compacted datastructures, which have been used for instance to find frequent items in data streams  [3]. Unfortunately sketches are not optimized for set intersection. Privacy has today become a major concern for many information systems.
CONCLUSION We have proposed GoldFinger, a new compact and fast-to-compute representation of datasets which accelerates the computation of Jaccard's index. We have illustrated the effectiveness of GoldFinger on KNN graph construction. As a side effect, GoldFinger also protects users' privacy for free. Our extensive evaluation shows that GoldFinger is able to drastically accelerate the construction of KNN graphs against the native versions of prominent KNN construction algorithms such as NNDescent or LSH while incurring a small to moderate loss in quality, and close to no overhead in dataset preparation compared to the state of the art. We have also precisely characterized the privacy protection provided by GoldFinger in terms of k-anonymity and ℓ-diversity.
11 0. 03 LSH 144 141 2. 1 0. 98 0. 96 0. 02 Brute Force 100 46 54. 0 1. 0 0. 82 0. 18 DBLP Hyrec NNDescent 31 46 27 24 41. 3 0. 86 0. 81 22. 6 0. 98 0. 82 0. 05 0. 16 LSH 40 38 2. 6 0. 87 0. 86 0. 01 Gowalla Brute Force 160 Hyrec 39 NNDescent 45 LSH 30 54 22 26 27 66. 3 43. 6 0. 95 0. 78 1. 0 0. 78 42. 2 1. 0 0. 79 3. 7 0. 87 0. 82 0. 22 0. 17 0. 21 0. 05 GoldFinger yields the shortest computation times across all datasets (in bold), yielding gains (gain) of up to 78. 9% against native algorithms. The loss in quality is at worst moderate, ranging from 0. 22 to an improvement of 0. 11.
Likewise, every group of learners has specific learning needs. Teachers who would like to investigate their groups' language needs might find it convenient to create and use a shorter version of the learner questionnaire I used for this study. In our particular case, the next step would be to design task-based syllabi per level of proficiency, taking into consideration the task characteristics outlined all through this chapter. To do so, I would further dive into learners' and teachers' reflections about the most efficient English activities. I believe detailed analysis of this qualitative data would undoubtedly enlighten course design -potentially the design of a database of ready-made language teaching units. Finally, great NA effectiveness would be achieved if the informed PE curriculum clearly stipulated the learning outcomes specifying what learners can do in English -another very often neglected dimension in language programs.
Participants I focused on two case studies of students who identified themselves as L2 French learners and were enrolled in an intermediate French class, the final course of the basic language sequence at the university. Criteria for selecting these students included their commitment to the class, their motivation in learning the target language and culture, and their high level of participation in class. By selecting these two case studies, I intended to present a contrastive sample of how students in the class had chosen to develop their digital story with specific semiotics in mind. These examples aim to illustrate possible learning strategies that students, consciously or unconsciously, implemented to achieve their projects. Born in Mexico, Maïze (pseudonym) arrived in the United States with her family when she was seven years old.
At the time of the study, she was 19 years old, a sophomore majoring in psychology and minoring in French. In a postsemester interview, Maïze tied her interest in French to her belief that learning a language expands creativity and cultural knowledge. As a native speaker of Spanish, she enjoyed comparing the differences of the languages. Raised within American culture with Hispanic traditions, she was often able to grasp the similarities between the French and the Mexican cultures and see how they differed from American culture. Maïze's DS is entitled "Choses Oubliées [Forgotten Things]" and is intended to raise "a global controversy" that she described as follows. "Likes, hearts, followers and subscribers. Every day, these things fill up our thoughts; they intoxicate our beliefs and influence us to behave in a way that the rest of society expects and wants us to behave".
Textual mode Fanny primarily concentrated her DS on her writing and decided to add captions to her PowerPoint slides to reach both the L2 and L1 audience. She demonstrated knowledge of narratology and used writing strategies to communicate her story. She had higher L2 proficiency than her peers and was committed to mastering the writing portions of her digital story by submitting multiple drafts (4) to receive as much feedback as possible. Conversely, Maïze made little use of the textual mode in her DS. She only used it to state credits and acknowledgments, which were displayed at the end of the project. She did not want to use English captions to reach a broader audience and advocated in her interview: "I think my message would reach more people if I also used the English language, but I also think sticking to the French language might intrigue more people and make them focus more on the actual video rather than the voiceover.
Oral/aural mode A piece of music and a recorded voiceover represent the aural mode of Maïze's project. In her interviews, she explained that selecting music was difficult, and she had to change it twice. She avoided music with too fast or too slow a beat or lyrics that could distract her audience. She played one song throughout her project. Her voiceover in French presents structured sentences to mitigate the language barrier. In the postquestionnaire, she wrote, "I didn't think complex ideas could be expressed in another language. Especially if that language was choppy and not fully developed. But then, I found a way to voice those complex ideas in a simpler manner". Maïze created short and concise sentences in L2 to deliver her message, and worked on her intonation to express rhythm, melody, and beat and emphasize the limited amount of words she used.
During an interview, Maïze explained, "I kind of had really good pictures that I wanted definitively put in there, and some good lines that I wanted to put in there". Thus, Maïze had to stretch out sentences of the voiceover and played around with the aural and visual modes during the editing phase to finalize the project the way she envisioned it. Furthermore, Maïze emphasized her overall message within two or three modes of communication. For instance, one of Maïze's pictures represented "vanity", and the words she wanted to emphasize in her narration were the two adjectives "vain" and "negative". In order to highlight both words, she drastically accentuated her intonation when pronouncing them. Taken with TechSmith's software Camtasia, Figure  3  represents the transduction of meanings produced by Maïze from one mode -visual -to another -oral.
Discussion In this study, I did not analyze language proficiency and instead examined the use of the target language with equal attention to other semiotic resources. The study suggests that students made intentional language and semiotic choices to construct meanings and express opinions. Maïze realized that sharing her point of view could be difficult in French despite her high proficiency. To mitigate challenges, she committed to work on diverse linguistic designs, including intonation, personal pronouns, and present tense. She chose to make short and well-structured sentences that she used as a pattern. Her vocabulary choices were selective and precise, which allowed her to sharpen her intonation and to emphasize specific words by stretching them out and orchestrating them with her visuals.
Consequently, some school directors and researchers are advocating for and implementing specific programs in early second language teaching (see Cnesco, 2019, and https://www. education. gouv. fr/bo/19/Hebdo22/MENE1915455N. htm). interactional competences: I present results from my own projects on reading at home  (Beaupoil-Hourdel, 2017; Beaupoil-Hourdel, Leroy-Collombel, & Morgenstern, 2019)  and propose qualitative multimodal analyses of the data to account for the participation framework and content of SBR activities. Corpus-based analyses of parent-child SBR activities at home will contribute to show how the parents in the corpus naturally and spontaneously engaged in SBR activities with their children. Third, based on my analyses, I draw some guidelines for the professionalization of preservice teachers who are trained in universities, keeping in mind that the ecology of reading books at home significantly differs from that of reading books in class.
Telling stories and reading books are common social practices in corpora of adult-child spontaneous interactions in families from an upper-middleclass background (as frequently evidenced in the CHILDES L1 Database). Interestingly, parents often use books with young children who cannot read. In these families, books are used for interaction and transmission of cultural knowledge about the world. Previous research has shown that routines of SBR activities  (Beaupoil-Hourdel, 2017; Cameron-Faulkner & Noble, 2013; Noble, Cameron-Faulkner, & Lieven, 2018; Payne, Whitehurst, & Angell, 1994)  trigger lexical development  (Payne et al. , 1994)  and grammatical development  (Cameron-Faulkner & Noble, 2013). Snow et al. (1976)  and Hoff-Ginsberg (1992) have shown that language used during SBR involves a higher mean length of utterance than in spontaneous adult-child dyadic interactions, suggesting that both the parents and the child produce longer turns in the context of SBR than during spontaneous interactions.
Data collection and analysis In this study I analyze the interaction framework of adult-child book reading situations at home and draw implications for teacher education. I use four longitudinal corpora of two monolingual French-speaking children (Madeleine and Anaé), and two monolingual English-speaking children (Ellie and Scarlett) filmed at home 4. The goal of this study is to draw a link between routines of L1 story-reading activities at home and L2 teaching activities at school with children under the age of six. The children filmed in their family in interaction with their parent were videorecorded one hour a month from ten months to four years old in spontaneous and natural interaction. I use three corpora from the Paris Corpus  (Morgenstern, 2009; Morgenstern & Parisse, 2012 , 2017)  and one dataset collected in London by Sam Green (UCL ESRC).