Relevant bibliographies by topics / Retrievers – Training

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Academic literature on the topic 'Retrievers – Training'

Author: Grafiati
Published: 26 July 2024

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Journal articles on the topic "Retrievers – Training"

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Fu, Xuan, Jiangnan Du, Hai-Tao Zheng, Jianfeng Li, Cuiqin Hou, Qiyu Zhou, and Hong-Gee Kim. "SS-BERT: A Semantic Information Selecting Approach for Open-Domain Question Answering." Electronics 12, no. 7 (April 3, 2023): 1692. http://dx.doi.org/10.3390/electronics12071692.

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Open-Domain Question Answering (Open-Domain QA) aims to answer any factoid questions from users. Recent progress in Open-Domain QA adopts the “retriever-reader” structure, which has proven effective. Retriever methods are mainly categorized as sparse retrievers and dense retrievers. In recent work, the dense retriever showed a stronger semantic interpretation than the sparse retriever. When training a dual-encoder dense retriever for document retrieval and reranking, there are two challenges: negative selection and a lack of training data. In this study, we make three major contributions to this topic: negative selection by query generation, data augmentation from negatives, and a passage evaluation method. We prove that the model performs better by focusing on false negatives and data augmentation in the Open-Domain QA passage rerank task. Our model outperforms other single dual-encoder rerankers over BERT-base and BM25 by 0.7 in MRR@10, achieving the highest Recall@50 and the max Recall@1000, which is restricted by the BM25 retrieval results.
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Söderlund, Ella-Erika, Heikki Kyröläinen, Outi M. Laitinen-Vapaavuori, and Heli K. Hyytiäinen. "Proposed Protocol for Field Testing of Endurance Fitness of Young Labrador Retrievers." Methods and Protocols 6, no. 4 (June 28, 2023): 61. http://dx.doi.org/10.3390/mps6040061.

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The number of dogs and, with it, dog sports are growing in popularity, and the training of dogs begins at an early age. Although fitness testing is an imperative part of purposeful training and sports, to our knowledge, no objective field tests are available for measuring young dogs’ endurance fitness. The aim of this study is to describe a simple, easy-to-repeat, and inexpensive way to test training intervention effects on endurance fitness in young Labrador Retrievers. Healthy client-owned 16-week-old Labrador Retrievers will be recruited and divided into test and control groups. The test group will have an eight-week training program followed by a four-week detraining period, while the control group will live a normal puppy life. All dogs will be tested for endurance fitness four times at four-week intervals: at baseline, one month later, two months later at the end of the training period, and one month after ending the training program. Each of the four testing sessions will be identical and will consist of four measurements of heart rate (HR) and blood lactate (BL): at baseline, after trotting 1000 m, after sprinting 200 m, and at recovery 5–8 min after the sprint. The training-induced changes in endurance fitness are evaluated by changes in HR and heart rate recovery times (HRR), BL, and running times.
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Steiss, J., H. A. Ahmad, P. Cooper, and C. Ledford. "Physiologic Responses in Healthy Labrador Retrievers during Field Trial Training and Competition." Journal of Veterinary Internal Medicine 18, no. 2 (March 2004): 147–51. http://dx.doi.org/10.1111/j.1939-1676.2004.tb00153.x.

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Luo, Man, Arindam Mitra, Tejas Gokhale, and Chitta Baral. "Improving Biomedical Information Retrieval with Neural Retrievers." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11038–46. http://dx.doi.org/10.1609/aaai.v36i10.21352.

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Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an important function in the biomedical domain, where content and sources of scientific knowledge may evolve rapidly. Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. First, to tackle the relative lack of data in the biomedical domain, we propose a template-based question generation method that can be leveraged to train neural retriever models. Second, we develop two novel pre-training tasks that are closely aligned to the downstream task of information retrieval. Third, we introduce the ``Poly-DPR'' model which encodes each context into multiple context vectors. Extensive experiments and analysis on the BioASQ challenge suggest that our proposed method leads to large gains over existing neural approaches and beats BM25 in the small-corpus setting. We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.
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Boiocchi, S., L. Vezzoni, V. Bronzo, F. Rossi, and A. Vezzoni. "Radiographic changes of the pelvis in Labrador and Golden Retrievers after juvenile pubic symphysiodesis." Veterinary and Comparative Orthopaedics and Traumatology 26, no. 03 (2013): 218–25. http://dx.doi.org/10.3415/vcot-12-06-0074.

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SummaryObjectives: The hypothesis of this study was that juvenile pubic symphysiodesis (JPS) results in pelvic changes that can be identified radiographically in adult dogs.Methods: The medical records at the Clinica Veterinaria Vezzoni were searched for standard ventro-dorsal views of the pelvis of adult Labrador and Golden Retrievers that had undergone JPS or had not undergone surgery. The objective assessment of radiographs included the analysis of various pelvic measurements. Subjective evaluation of radiographs was undertaken by 18 specialists and 21 general practitioners and was based on five criteria relating to 1) the acetabular fossae, 2) the pubic symphysis, 3) the margin of the cranial pubic area, 4) the pubic rami, and 5) the obturator foramen.Results: The radiographs of 42 Labrador Retrievers and 16 Golden Retrievers were evaluated. The most useful criteria were the radiographic measurement of the shape of the obturator foramen and two different ratios of length to width of the pubic rami; these values were significantly smaller in dogs after JPS. The pelvic canal width was the same in both groups. All objective measurements were repeatable within and between evaluators. The most reliable subjective criterion was number 4, followed by number 5 in Golden Retrievers and by 2 in Labrador Retrievers.Conclusion: Our objective and subjective evaluations were simple and yielded useful and repeatable results. There was no significant difference between general practitioners and specialists with regard to subjective evaluation, which indicates that these evaluation criteria can be used by small animal clinicians after minimal training.
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Miller, Benjamin F., Sarah E. Ehrlicher, Joshua C. Drake, Frederick F. Peelor, Laurie M. Biela, Shannon Pratt-Phillips, Michael Davis, and Karyn L. Hamilton. "Assessment of protein synthesis in highly aerobic canine species at the onset and during exercise training." Journal of Applied Physiology 118, no. 7 (April 1, 2015): 811–17. http://dx.doi.org/10.1152/japplphysiol.00982.2014.

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Canis lupus familiaris, the domesticated dog, is capable of extreme endurance performance. The ability to perform sustained aerobic exercise is dependent on a well-developed mitochondrial reticulum. In this study we examined the cumulative muscle protein and DNA synthesis in groups of athletic dogs at the onset of an exercise training program and following a strenuous exercise training program. We hypothesized that both at the onset and during an exercise training program there would be greater mitochondrial protein synthesis rates compared with sedentary control with no difference in mixed or cytoplasmic protein synthesis rates. Protein synthetic rates of three protein fractions and DNA synthesis were determined over 1 wk using 2H2O in competitive Alaskan Huskies and Labrador Retrievers trained for explosive device detection. Both groups of dogs had very high rates of skeletal muscle protein synthesis in the sedentary state [Alaskan Huskies: Mixed = 2.28 ± 0.12, cytoplasmic (Cyto) = 2.91 ± 0.10, and mitochondrial (Mito) = 2.62 ± 0.07; Labrador Retrievers: Mixed = 3.88 ± 0.37, Cyto = 3.85 ± 0.06, and Mito = 2.92 ± 0.20%/day]. Mitochondrial (Mito) protein synthesis rates did not increase at the onset of an exercise training program. Exercise-trained dogs maintained Mito protein synthesis during exercise training when mixed (Mixed) and cytosolic (Cyto) fractions decreased, and this coincided with a decrease in p-RpS6 but also a decrease in p-ACC signaling. Contrary to our hypothesis, canines did not have large increases in mitochondrial protein synthesis at the onset or during an exercise training program. However, dogs have a high rate of protein synthesis compared with humans that perhaps does not necessitate an extra increase in protein synthesis at the onset of aerobic exercise training.
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Zhou, Yucheng, Tao Shen, Xiubo Geng, Chongyang Tao, Jianbing Shen, Guodong Long, Can Xu, and Daxin Jiang. "Fine-Grained Distillation for Long Document Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 19732–40. http://dx.doi.org/10.1609/aaai.v38i17.29947.

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Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder. However, in contrast to passages or sentences, retrieval on long documents suffers from the \textit{scope hypothesis} that a long document may cover multiple topics. This maximizes their structure heterogeneity and poses a granular-mismatch issue, leading to an inferior distillation efficacy. In this work, we propose a new learning framework, fine-grained distillation (FGD), for long-document retrievers. While preserving the conventional dense retrieval paradigm, it first produces global-consistent representations crossing different fine granularity and then applies multi-granular aligned distillation merely during training. In experiments, we evaluate our framework on two long-document retrieval benchmarks, which show state-of-the-art performance.
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Long, Xinwei, Jiali Zeng, Fandong Meng, Zhiyuan Ma, Kaiyan Zhang, Bowen Zhou, and Jie Zhou. "Generative Multi-Modal Knowledge Retrieval with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 18733–41. http://dx.doi.org/10.1609/aaai.v38i17.29837.

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Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of their effectiveness and training efficiency, especially when it comes to training and integrating multiple retrievers to handle multi-modal queries. In this paper, we propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases, even when trained with limited data. We retrieve knowledge via a two-step process: 1) generating knowledge clues related to the queries, and 2) obtaining the relevant document by searching databases using the knowledge clue. In particular, we first introduce an object-aware prefix-tuning technique to guide multi-grained visual learning. Then, we align multi-grained visual features into the textual feature space of the LLM, employing the LLM to capture cross-modal interactions. Subsequently, we construct instruction data with a unified format for model training. Finally, we propose the knowledge-guided generation strategy to impose prior constraints in the decoding steps, thereby promoting the generation of distinctive knowledge clues. Through experiments conducted on three benchmarks, we demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
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Affenzeller, Nadja. "Dog–Human Play, but Not Resting Post-Learning Improve Re-Training Performance up to One Year after Initial Task Acquisition in Labrador Retriever Dogs: A Follow-On Study." Animals 10, no. 7 (July 21, 2020): 1235. http://dx.doi.org/10.3390/ani10071235.

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Arousing and emotional situations can improve cognitive performance and the memorability of events. Recently, the enhancement of training performance in Labrador Retriever dogs through 30 min of dog–human play immediately after acquiring a novel task, when compared to a resting period, was demonstrated. This follow-on study used the same pseudo-randomized, counterbalanced, between-subject study design, and 11 Labrador Retrievers were re-trained in the identical two-choice discrimination paradigm after a period of 1 year. The playful activities group needed significantly less trials and made significantly less errors to successfully reach the re-training criterion (Mann–Whitney U test, critical value of U at p < 0.05 is 5, U = 5, Z = 1.73, p = 0.04 and U = 4.5, Z = 1.8, p = 0.03, respectively). Following model simplification of a multiple factor/covariate general linear model analysis, the type of intervention, the number of trials needed to re-learn the task after 24 h, the average heart rate during the intervention a year ago, and age were significantly correlated to the number of trials and errors needed to resolve the task. A significant difference due to intervention allocation (heart rate during the intervention, trials needed to re-learn the task after 24 h) between the groups was confirmed. Age did not significantly differ between the groups; nevertheless, the effects of ageing cannot be fully excluded, given the low sample size. No effects of the trainer and of the cortisol concentrations (of the previous year) were observed. This is the first evidence that post-training activity may influence memory up to 1 year after task acquisition.
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Riser, Molly M., Eldin Leighton, Jane Russenberger, Breno O. Fragomeni, and Caroline Moser. "189 Single-Step Gblup Evaluation for Behavioral Traits in Labrador Retrievers Used as Guide Dogs." Journal of Animal Science 101, Supplement_3 (November 6, 2023): 96–97. http://dx.doi.org/10.1093/jas/skad281.118.

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Abstract The objective of this study was to evaluate the accuracy of prediction in a genomic selection program for behavior traits in a population of Labrador Retrievers used as service dogs. Phenotypic data were collected in 4841 Labrador Retrievers with ages ranging from 3 months to 2.5 years, for 17 phenotypes from the International Working Dog Registry behavior checklist. The behavior checklist is a document that standardizes a scoring system for the reaction of an individual dog to environmental stimulus. Such scores are used to assess behavior and suitability of a dog for training. Pedigree contained 23,593 animals with birth dates ranging from 1991 to 2019. Genomic data were available for 457 individuals and obtained by low-pass whole genome sequences and reduced to a 250K SNP chip. Breeding values were calculated using a single trait animal model that included the fixed effects of sex, year of birth, and a contemporary group that included month/year of behavior test and organization that hosted the test. Variance components were estimated using AIREML. Genomic information was included in the model under a single-step GBLUP (ssGBLUP) approach by substituting the pedigree numerator relationship matrix with a matrix that combined pedigree and genomic relationships. Additionally, the genomic relationship matrix was modified under a weighted ssGBLUP (wssGBLUP) approach that allowed SNPs to have different distributions. Accuracies were evaluated in a 5-fold cross-validation that simulated a forward-in-time prediction. Heritabilities were low to moderate on all traits and varied from 0.025 to 0.37. Prediction based solely on pedigree information averaged 0.49 and ranged from 0.34 to 0.69. ssGBLUP increased the average accuracy to 0.55 and ranged from 0.34 to 0.72. Genomic estimated breeding values were more accurate than those computed with pedigrees for most traits. Gains in accuracy were limited by the small number of genotyped animals and are expected to increase as more animals are genotyped. The differences seen between the ssGBLUP approach and the wssGBLUP were minimal, and accuracies decreased after the second iteration. Those results indicate that behavior traits in this population are likely highly polygenic and would not benefit from weighted approaches. However, interpretation may change, as the limitations of the current study are due to the small number of genotyped individuals. Better SNP weight estimates may occur with more animals enrolled in the program, and with that a better description of the genetic architecture of the traits. The gains in accuracy show that genomic selection can help with improvement by identifying which young dogs have the highest genetic merit for the desired traits and are the best choices to keep as replacement breeders. The use of ssGBLUP is adequate for this canine data where not all animals are genotyped, and its use is recommended in selection programs focused on service dogs.
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Books on the topic "Retrievers – Training"

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Quinn, Tom. The working retrievers: The training, care, and handling of retrievers for hunting and field trails. New York, N.Y: Lyons Press, 1998.

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Sucher, Jaime J. Golden retrievers: Everything about purchase, care, nutrition, breeding, behavior, and training. 2nd ed. Hauppauge, NY: Barron's, 1995.

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Golden retrievers: Everything about purchase, care, nutrition, breeding, behavior, and training. New York: Barron's, 1987.

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Retriever Training. Shrewsbury: Swan Hill Press, 1992.

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Spencer, James B. Retriever training tests. 2nd ed. Loveland, Colo: Alpine Publications, 1997.

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Barbara, Branstad, and Whicker Sandra, eds. Retriever working certificate training. [Loveland, Colo.]: Alpine Publications, 1986.

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Dobbs, Jim. Tri-Tronic's retriever training. Tucson, Ariz: Tri-Tronics, 1993.

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Training your retriever. New York: Putnam, 1991.

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George, Bobby N. Training retrievers: The Cotton Pershall method. Traverse City, Mich: Countrysport Press, 1990.

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Spencer, James B. Training retrievers for marshes and meadows. 2nd ed. Loveland, Colo: Alpine Publications, 1998.

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Book chapters on the topic "Retrievers – Training"

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Tran, Si, Nasrullah Khan, Emmanuel Charles Kimito, Akeem Pedro, Mehrtash Soltani, Rahat Hussain, Taehan Yoo, and Chansik Park. "Extracting Information from Construction Safety Requirements Using Large Language Model." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 761–67. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.76.

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The construction industry has long been recognized for its complex safety regulations, which are essential to ensure the well-being of on-site employees. However, navigating these regulations and ensuring compliance can be challenging due to the volume and complexity of the documents involved. This study proposes a novel approach to extracting information from construction safety documents utilizing Large Language Models (LLM), called CSQA, to provide real-time, precise answers to queries related to safety regulations. The approach comprises three modules: (1) the construction safety investigation module (CSI) collects safety regulations for building the information needed. By leveraging a collection of safety regulation PDFs, the system follows a process of text extraction, preprocessing, and global indexing for efficient search. (2) The safety condition identification module (SCI) retrieves the CSI database; after that, the LLM, with its extensive training, processes user queries, searches the indexed regulations, and retrieves pertinent information. (3) the safety information delivery (SID) would provide the answer to the user and incorporate a feedback mechanism to further refine system accuracy based on user responses. Preliminary evaluations reveal the system's superior performance over traditional search engines, owing to its ability to grasp query context and nuances. The CSQA presents a promising method for accessing safety regulations, with potential benefits including reduced non-compliance incidents, enhanced worker safety, and streamlined regulatory consultations in construction
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Tran, Si, Nasrullah Khan, Emmanuel Charles Kimito, Akeem Pedro, Mehrtash Soltani, Rahat Hussain, Taehan Yoo, and Chansik Park. "Extracting Information from Construction Safety Requirements Using Large Language Model." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 761–67. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.76.

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The construction industry has long been recognized for its complex safety regulations, which are essential to ensure the well-being of on-site employees. However, navigating these regulations and ensuring compliance can be challenging due to the volume and complexity of the documents involved. This study proposes a novel approach to extracting information from construction safety documents utilizing Large Language Models (LLM), called CSQA, to provide real-time, precise answers to queries related to safety regulations. The approach comprises three modules: (1) the construction safety investigation module (CSI) collects safety regulations for building the information needed. By leveraging a collection of safety regulation PDFs, the system follows a process of text extraction, preprocessing, and global indexing for efficient search. (2) The safety condition identification module (SCI) retrieves the CSI database; after that, the LLM, with its extensive training, processes user queries, searches the indexed regulations, and retrieves pertinent information. (3) the safety information delivery (SID) would provide the answer to the user and incorporate a feedback mechanism to further refine system accuracy based on user responses. Preliminary evaluations reveal the system's superior performance over traditional search engines, owing to its ability to grasp query context and nuances. The CSQA presents a promising method for accessing safety regulations, with potential benefits including reduced non-compliance incidents, enhanced worker safety, and streamlined regulatory consultations in construction
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Muhamed, Aashiq, Sriram Srinivasan, Choon-Hui Teo, Qingjun Cui, Belinda Zeng, Trishul Chilimbi, and S. V. N. Vishwanathan. "Web-Scale Semantic Product Search with Large Language Models." In Advances in Knowledge Discovery and Data Mining, 73–85. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33380-4_6.

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AbstractDense embedding-based semantic matching is widely used in e-commerce product search to address the shortcomings of lexical matching such as sensitivity to spelling variants. The recent advances in BERT-like language model encoders, have however, not found their way to realtime search due to the strict inference latency requirement imposed on e-commerce websites. While bi-encoder BERT architectures enable fast approximate nearest neighbor search, training them effectively on query-product data remains a challenge due to training instabilities and the persistent generalization gap with cross-encoders. In this work, we propose a four-stage training procedure to leverage large BERT-like models for product search while preserving low inference latency. We introduce query-product interaction pre-finetuning to effectively pretrain BERT bi-encoders for matching and improve generalization. Through offline experiments on an e-commerce product dataset, we show that a distilled small BERT-based model (75M params) trained using our approach improves the search relevance metric by up to 23% over a baseline DSSM-based model with similar inference latency. The small model only suffers a 3% drop in relevance metric compared to the 20x larger teacher. We also show using online A/B tests at scale, that our approach improves over the production model in exact and substitute products retrieved.
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Sun, Wei, Shaoxiong Ji, Tuulia Denti, Hans Moen, Oleg Kerro, Antti Rannikko, Pekka Marttinen, and Miika Koskinen. "Weak Supervision and Clustering-Based Sample Selection for Clinical Named Entity Recognition." In Lecture Notes in Computer Science, 444–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43427-3_27.

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AbstractOne of the central tasks of medical text analysis is to extract and structure meaningful information from plain-text clinical documents. Named Entity Recognition (NER) is a sub-task of information extraction that involves identifying predefined entities from unstructured free text. Notably, NER models require large amounts of human-labeled data to train, but human annotation is costly and laborious and often requires medical training. Here, we aim to overcome the shortage of manually annotated data by introducing a training scheme for NER models that uses an existing medical ontology to assign weak labels to entities and provides enhanced domain-specific model adaptation with in-domain continual pretraining. Due to limited human annotation resources, we develop a specific module to collect a more representative test dataset from the data lake than a random selection. To validate our framework, we invite clinicians to annotate the test set. In this way, we construct two Finnish medical NER datasets based on clinical records retrieved from a hospital’s data lake and evaluate the effectiveness of the proposed methods. The code is available at https://github.com/VRCMF/HAM-net.git.
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Pacheco, Renata, Iryna Skulska, Ana Catarina Sequeira, and M. Conceição Colaço. "Wildfire Education: A Review Across the Globe." In Fire Hazards: Socio-economic and Regional Issues, 29–41. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-50446-4_3.

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AbstractRecent projections suggest that wildfires will occur more often and with higher intensity due to the changing climate. In this context, it is vital to educate the population to be ready and prepared to deal with these events. This book chapter reviews the state of the art of educational materials on wildfires worldwide that are available online. A total of 225 references on the matter were retrieved. The materials are from all five continents, involving 36 countries and written in 23 languages. Most of them are from regions with a Mediterranean climate with fire-prone ecosystems in which, for the last decades, wildfires have negatively affected the population. Regarding the target audience, most materials retrieved focused on the general public (about 48%), followed by students from various age groups (around 40%). Written documents, websites, and videos are the most frequent materials for the general public. As for students, a greater variability of pedagogical materials is available, ranging from mobile phone applications and digital and experimental activities to slides for classes and reading materials. The remaining materials focus on the rural population and firefighters’ training. Most references present the main concepts and ecological aspects of fire, along with safety and prevention measures. However, few discuss climate change, recovery, and socio-economic or health concerns. This gap should be addressed in the future wildfire educational materials to better prepare and inform society.
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Dykes, Nathan, Stephanie Evert, Philipp Heinrich, Merlin Humml, and Lutz Schröder. "Finding Argument Fragments on Social Media with Corpus Queries and LLMs." In Robust Argumentation Machines, 163–81. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63536-6_10.

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AbstractWe are concerned with extracting argumentative fragments from social media, exemplified with a case study on a large corpus of English tweets about the UK Brexit referendum in 2016. Our overall approach is to parse the corpus using dedicated corpus queries that fill designated slots in predefined logical patterns. We present an inventory of logical patterns and corresponding queries, which have been carefully designed and refined. While a gold standard of substantial size is difficult to obtain by manual annotation, our queries can retrieve hundreds of thousands of examples with high precision. We show how queries can be combined to extract complex nested statements relevant to argumentation. We also show how to proceed for applications needing higher recall: high-precision query matches can be used as training data for an LLM classifier, and the trade-off between precision and recall can be freely adjusted with its cutoff threshold.
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Trappey, A. J. C., C. V. Trappey, and Samuel Shih. "Logo Image Retrievals Using Deep Embedding Learning." In Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210088.

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A logo is s graphical emblem or mark used as an identification for a company and its products and services. Logos are legally protected as intellectual properties (IPs) if registered as trademarks (TMs). LogosTM are widely distributed online nowadays in the digital economy. Due to their wide distributions online, the constant checking of TM legal usages becomes extremely challenging in the TM registration and protection system. The fact that users can easily imitate the registered TM logo designs casts serious IP legal issue, which highlights the importance of developing an automatic logo image retrieval system. Considering the complexity of TM visual semantics, this research proposes a deep embedding learning for logo image similarity analysis using triplet-network. We propose the optimization of sampling parameters to improve the TM image retrieval performance with robust model. The research aims to reduce discrepancy between human visual interpretation. This transdisciplinary engineering research incorporates deep learning (DL) modeling and TM legal analysis for image-centric TM protection. To demonstrate the model performance, more than 10,000 images for model training and 3000 images for model testing are adopted from Logo-2K+ database. Image retrieval performance shows excellent results with recall@10 exceeding 93%.
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AI-Saleem, Naifa Eid, Mohammed Nasser Al-Saqri, and Aysha Sultan Al-Badri. "The Reality of Use of WhatsApp as a Tool for Distance Education in Teaching and Learning." In Advanced Online Education and Training Technologies, 200–213. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7010-3.ch012.

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This research aims to investigate the reality of WhatsApp use among faculty members at the Department of Information Studies (IS) at Sultan Qaboos University (SQU) in the Sultanate of Oman as a tool for distance education (DE) and as a tool for teaching and learning. The study also focuses on the information retrieved on WhatsApp teaching groups and its use. Data was collected through the interview method. The results of the study showed that three faculty members out of nine are using WhatsApp in teaching and learning. They use it for class discussions and explanations of projects. The study showed that the three faculty members in the Department of IS who used WhatsApp in teaching and learning are using it in general to communicate with the students and also for educational purpose. In addition, the study showed a relationship between age, nationality, and specialization. The study also proved that students used WhatsApp as an open source of information.
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Armstrong, Anne-Marie. "Learning Objects for Employee Training and Competency Development." In Learning Objects for Instruction, 159–73. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-334-0.ch010.

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Learning objects are being used more and more by the corporate training world. Their acceptance by corporate training can be attributed in part to the fact that they provided those departments with a system and tools that they could present to their decision makers—a system that aligned with corporate goals. Some of those goals included the need to train a global workforce and the need to do it in an effective, competitive, and efficient manner. The examples provided demonstrate how and why learning object systems have found success in different corporations. First content was chosen that could be developed, parsed, stored, and retrieved. The content was both reusable and migratory. Next robust systems that allow the various learning audiences to access the content and use it for various purposes were built. And finally, the benefits to the various stakeholders were successfully marketed and accepted.
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Jaime-Diaz, Jesus, and Josie Méndez-Negrete. "A Guide for Deconstructing Social Reproduction: Pedagogical Conocimientos within the Context of Teacher Education." In Teacher Education in the 21st Century - Emerging Skills for a Changing World. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96213.

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As the mosaic of student demographics continue to change into the 21st century, teacher credential training programs must necessarily prepare educators to be culturally affirming and responsive to the equitable schooling of students. Through pedagogical conocimientos, educators-in-training may rely on self-reflexive methodologies, which facilitates the engagement of self and others in interaction, as they collectively retrieve family legacies, focusing on gathering histories on their family’s origins, language, religion, work, education, and migration. This prepares future teachers to unearth and examine internalized prejudices, traumas, and stereotypes, to thus counter and contest deficit thinking and distorted views of student populations, beginning with them. This chapter introduces pedagogical conocimientos, illustrating the praxis as it problematizes social reproduction in the context of schooling.
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Conference papers on the topic "Retrievers – Training"

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Shi, Peng, Rui Zhang, He Bai, and Jimmy Lin. "Cross-Lingual Training of Dense Retrievers for Document Retrieval." In Proceedings of the 1st Workshop on Multilingual Representation Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.mrl-1.24.

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Sachan, Devendra, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, and Bryan Catanzaro. "End-to-End Training of Neural Retrievers for Open-Domain Question Answering." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.519.

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Salemi, Alireza, Mahta Rafiee, and Hamed Zamani. "Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering." In ICTIR '23: The 2023 ACM SIGIR International Conference on the Theory of Information Retrieval. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3578337.3605137.

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Campos, Daniel, Alessandro Magnani, and Chengxiang Zhai. "Quick Dense Retrievers Consume KALE: Post Training KullbackLeibler Alignment of Embeddings for Asymmetrical dual encoders." In Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.sustainlp-1.4.

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Seonwoo, Yeon, Sang-Woo Lee, Ji-Hoon Kim, Jung-Woo Ha, and Alice Oh. "Weakly Supervised Pre-Training for Multi-Hop Retriever." In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-acl.62.

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Li, Ziran, Zibo Lin, Ning Ding, Hai-Tao Zheng, and Ying Shen. "Triple-to-Text Generation with an Anchor-to-Prototype Framework." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/523.

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Generating a textual description from a set of RDF triplets is a challenging task in natural language generation. Recent neural methods have become the mainstream for this task, which often generate sentences from scratch. However, due to the huge gap between the structured input and the unstructured output, the input triples alone are insufficient to decide an expressive and specific description. In this paper, we propose a novel anchor-to-prototype framework to bridge the gap between structured RDF triples and natural text. The model retrieves a set of prototype descriptions from the training data and extracts writing patterns from them to guide the generation process. Furthermore, to make a more precise use of the retrieved prototypes, we employ a triple anchor that aligns the input triples into groups so as to better match the prototypes. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both automatic and manual evaluation, demonstrating the benefit of learning guidance from retrieved prototypes to facilitate triple-to-text generation.
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Gao, Yifan, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, and Michael Lyu. "Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training." In Findings of the Association for Computational Linguistics: NAACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-naacl.92.

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Tao, Chongyang, Jiazhan Feng, Tao Shen, Chang Liu, Juntao Li, Xiubo Geng, and Daxin Jiang. "CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.174.

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Chen, Qianglong, Feng-Lin Li, Guohai Xu, Ming Yan, Ji Zhang, and Yin Zhang. "DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/567.

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Although pre-trained language models (PLMs) have achieved state-of-the-art performance on various natural language processing (NLP) tasks, they are shown to be lacking in knowledge when dealing with knowledge driven tasks. Despite the many efforts made for injecting knowledge into PLMs, this problem remains open. To address the challenge, we propose DictBERT, a novel approach that enhances PLMs with dictionary knowledge which is easier to acquire than knowledge graph (KG). During pre-training, we present two novel pre-training tasks to inject dictionary knowledge into PLMs via contrastive learning: dictionary entry prediction and entry description discrimination. In fine-tuning, we use the pre-trained DictBERT as a plugin knowledge base (KB) to retrieve implicit knowledge for identified entries in an input sequence, and infuse the retrieved knowledge into the input to enhance its representation via a novel extra-hop attention mechanism. We evaluate our approach on a variety of knowledge driven and language understanding tasks, including NER, relation extraction, CommonsenseQA, OpenBookQA and GLUE. Experimental results demonstrate that our model can significantly improve typical PLMs: it gains a substantial improvement of 0.5%, 2.9%, 9.0%, 7.1% and 3.3% on BERT-large respectively, and is also effective on RoBERTa-large.
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Uttrani, Shashank, Bhavik Kanekar, Aadhar Gupta, Harsh Katakwar, and Varun Dutt. "Evaluating Human Performance in a Complex Search-and-Retrieve Task." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001578.

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Prior research has investigated human performance in simple psychological tasks with a smaller cognitive workload. However, little is known about how humans learn in complex search-and-retrieve simulated environments. The primary objective of our research was to evaluate human performance in a complex search-and-retrieve environment. We developed a complex simulated environment, mimicking a military on-ground operation, using Unity 3D with targets and distractors. Fifty human participants were recruited to play the simulated game for 25 minutes. Participants were tasked to maximize their score by collecting targets items and avoiding distractor items available within the environment. The game's duration was divided into training and testing phases, which differed in terms of availability of feedback and the time duration (15 minutes for the training phase and 10 minutes for the test phase). In the training phase, the participants were allowed to navigate the environment to collect the items (14 targets and 7 distractors) with scores as feedback. Participants had to navigate the environment while collecting the items (28 targets and 14 distractors) to maximize their score without feedback. Results revealed a significant difference in the performance of human participants from the training phase to the test phase. The participants scored significantly more in the test phase without feedback than the training phase with feedback. Also, there was a significant increase in the proportion of targets collected over the time in both the train and test phases. We highlight the implications of developing simulation tools for training personnel in different tasks.
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Reports on the topic "Retrievers – Training"

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Donti, Olyvia, Andreas Konrad, Ioli Panidi, Petros Dinas, and Gregory Bogdanis. Is there a window of opportunity for flexibility development in youth? A systematic review with meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2021. http://dx.doi.org/10.37766/inplasy2021.9.0032.

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Review question / Objective: To examine if there is a difference in the effect of stretching training on flexibility during childhood (6-11 years of age) and adolescence (12-18 years of age). Condition being studied: We are going to examine whether there is a greater response to stretching training (i.e. ‘window of opportunity’) during childhood, compared with adolescence. Information sources: Two review team members will independently screen the titles and abstracts of the retrieved publications to select the eligible publications. One review team member will act as a referee in case of disagreement between the review team members. We will also ensure that any retracted publications are identified and excluded from the selection outcome. Furthermore, we will locate the full texts that will not be immediately accessible, via emails to the lead authors and/journals of publication. A full list of the excluded publications will be provided in the final version of the systematic review.
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Barros, Margarida, Cristiana Bessa, Isabel Mesquita, and Paula Queirós. The Expression of Epistemological Beliefs in Initial Teacher Education: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0131.

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Review question / Objective: The purpose of this systematic review is to scrutinize what is known about pre-service teachers’ epistemological beliefs in initial teacher training. The research questions which guided the review of these studies were: (Q1) What is the theoretical framework used? (Q2) What is the domain present in the research? (Q3) What have been the main purposes of the research? (Q4) Which have been the methodological procedures used to access epistemological beliefs? (Q5) What are the main research findings? Information sources: Five databases will be used to search and retrieve the articles: EBSCO, ERIC, Web of Science and SCOPUS. This review will not exclude any work based on the date of conclusion as it intends to understand and illustrate the overview of all the research carried out on the epistemological beliefs of pre-service teachers. This will allow access to the explanatory factors of the contours and manifestations that the EB assume in this training phase.
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Waldfogel, Julie M., Michael Rosen, Ritu Sharma, Allen Zhang, Eric B. Bass, and Sydney M. Dy. Making Healthcare Safer IV: Opioid Stewardship. Agency for Healthcare Research and Quality (AHRQ), December 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4opioid.

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Objectives. Opioid stewardship interventions promote the appropriate use of prescribed and ordered opioids to reduce the risk of opioid adverse events. Our main objectives were to determine the effectiveness of these interventions in healthcare settings on opioid prescribing and clinical outcomes (e.g., number of opioid prescriptions, opioid dosage, overdose, emergency department visits, and hospitalizations) including unintended consequences (e.g., changes in patient-reported pain intensity), and ways these interventions can be effectively implemented. Methods. We followed rapid review processes of the Agency for Healthcare Research and Quality Evidence-based Practice Center Program. We searched PubMed and the Cochrane Library to identify eligible systematic reviews from January 2019 to April 2023 and primary studies published from January 2016 to April 2023, supplemented by targeted gray literature searches. We included systematic reviews and studies that addressed opioid stewardship interventions implemented in healthcare settings in the United States and that reported on opioid prescribing and clinical outcomes. Findings. Our search retrieved 6,431 citations, of which 34 articles were eligible (including 1 overview of systematic reviews, 13 additional systematic reviews, 13 randomized controlled trials (RCTs) [reported in 14 articles] and 6 nonrandomized studies). Systematic reviews, mostly summarizing pre-post studies, included a wide variety of opioid stewardship practices that focused on patient and family engagement, healthcare organization policy, or clinician knowledge and behavior interventions, in inpatient, perioperative, emergency department, and ambulatory settings. RCTs addressed multicomponent interventions (typically a combination of prescriber education, care management and facilitated access to resources), and patient education and engagement, mainly in ambulatory chronic pain. Opioid stewardship practices involving clinical decision support or electronic health records, or multicomponent interventions (including for chronic pain) were associated with decreases in opioid prescribing or reduced doses and no increases in pain, emergency department visits, or hospitalizations (low strength of evidence for all outcomes). Patient engagement and education interventions had mixed results for opioid prescribing outcomes (insufficient strength of evidence) and no increases in pain, emergency department visits, or hospitalizations (low strength of evidence). The evidence was insufficient on other types of interventions and on outcomes of opioid refill requests and refills, patient satisfaction, or overdose. Barriers included lack of training, workload, gaps in communication, and inadequate access to nonpharmacological resources. Facilitators included clinician and patient acceptance of intervention components. Conclusions. Selected opioid stewardship interventions may be effective for reducing opioid prescribing and dosing without adversely affecting clinical outcomes overall, although strength of evidence was low. Unintended consequences were often not measured or not measured rigorously. Interventions to reduce opioid use should monitor unintended consequences and include access to nonpharmacological pain management resources with appropriate patient education and engagement.
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