Academic literature on the topic 'Attention based models'
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Journal articles on the topic "Attention based models"
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Zang, Yubin, Zhenming Yu, Kun Xu, Minghua Chen, Sigang Yang, and Hongwei Chen. "Fiber communication receiver models based on the multi-head attention mechanism." Chinese Optics Letters 21, no. 3 (2023): 030602. http://dx.doi.org/10.3788/col202321.030602.
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Qin, Chu-Xiong, and Dan Qu. "Towards Understanding Attention-Based Speech Recognition Models." IEEE Access 8 (2020): 24358–69. http://dx.doi.org/10.1109/access.2020.2970758.
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Cha, Peter, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, and Eun-Ah Kim. "Attention-based quantum tomography." Machine Learning: Science and Technology 3, no. 1 (2021): 01LT01. http://dx.doi.org/10.1088/2632-2153/ac362b.
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Abstract With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Vaswani et al (2017 NIPS) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.
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Fallahnejad, Zohreh, and Hamid Beigy. "Attention-based skill translation models for expert finding." Expert Systems with Applications 193 (May 2022): 116433. http://dx.doi.org/10.1016/j.eswa.2021.116433.
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Steelman, Kelly S., Jason S. McCarley, and Christopher D. Wickens. "Theory-based Models of Attention in Visual Workspaces." International Journal of Human–Computer Interaction 33, no. 1 (2016): 35–43. http://dx.doi.org/10.1080/10447318.2016.1232228.
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Thapa, Krishu K., Bhupinderjeet Singh, Supriya Savalkar, Alan Fern, Kirti Rajagopalan, and Ananth Kalyanaraman. "Attention-Based Models for Snow-Water Equivalent Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 22969–75. http://dx.doi.org/10.1609/aaai.v38i21.30337.
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Snow Water-Equivalent (SWE)—the amount of water available if snowpack is melted—is a key decision variable used by water management agencies to make irrigation, flood control, power generation, and drought management decisions. SWE values vary spatiotemporally—affected by weather, topography, and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporal complete data. While recent efforts have explored machine learning (ML) for SWE prediction, a number of recent ML advances have yet to be considered. The main contribution of this paper is to explore one such ML advance, attention mechanisms, for SWE prediction. Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both). We present a generic attention-based modeling framework for SWE prediction and adapt it to capture spatial attention and temporal attention. Our experimental results on 323 SNOTEL stations in the Western U.S. demonstrate that our attention-based models outperform other machine-learning approaches. We also provide key results highlighting the differences between spatial and temporal attention in this context and a roadmap toward deployment for generating spatially-complete SWE maps.
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王, 紫阳. "Wind Speed Prediction Based on Attention-Combined Models." Artificial Intelligence and Robotics Research 14, no. 02 (2025): 389–96. https://doi.org/10.12677/airr.2025.142038.
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AlOmar, Ban, Zouheir Trabelsi, and Firas Saidi. "Attention-Based Deep Learning Modelling for Intrusion Detection." European Conference on Cyber Warfare and Security 22, no. 1 (2023): 22–32. http://dx.doi.org/10.34190/eccws.22.1.1172.
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Cyber-attacks are becoming increasingly sophisticated, posing more significant challenges to traditional intrusion detection methods. The inability to prevent intrusions could compromise the credibility of security services, thereby putting data confidentiality, integrity, and availability at risk. In response to this problem, research has been conducted to apply deep learning (DL) models to intrusion detection, leveraging the new era of AI and the proven efficiency of DL in many fields. This study proposes a new intrusion detection system (IDS) based on DL, utilizing attention-based long short-term memory (AT-LSTM) and attention-based bidirectional LSTM (AT-BiLSTM) models. The time-series nature of network traffic data, which changes continuously over time, makes LSTM and BiLSTM particularly effective in handling intrusion detection. These models can capture long-term dependencies in the sequence of events, learn the patterns of normal network behaviour, and detect deviations from this behaviour that may indicate an intrusion. Also, the attention mechanism in the proposed models lets them make predictions based on the most important parts of the network traffic data. This is important for finding intrusions because network traffic data can have many different features, not all of which are important for finding an attack. The attention mechanism lets the models learn which features are most important for making accurate predictions, which improves their performance and efficiency. The UNSW-NB15 benchmark dataset is used in the study to measure and compare the effectiveness and reliability of the proposed system. This dataset contains normal and attack traffic data with a significant class imbalance. To address this issue, the study employs the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset, thus reducing the risk of overfitting to the majority class and improving the model's performance in detecting attacks. The performance evaluation results demonstrate that the proposed models achieved a detection rate of over 93%, indicating high precision in detecting intrusions. By harnessing the power of deep learning, these models can learn and adapt to new threats over time, thus ensuring data confidentiality, integrity, and availability in today's interconnected world.
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Tangsali, Rahul, Swapnil Chhatre, Soham Naik, Pranav Bhagwat, and Geetanjali Kale. "Evaluating Performances of Attention-Based Merge Architecture Models for Image Captioning in Indian Languages." Journal of Image and Graphics 11, no. 3 (2023): 294–301. http://dx.doi.org/10.18178/joig.11.3.294-301.
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Image captioning is a growing topic of research in which numerous advancements have been made in the past few years. Deep learning methods have been used extensively for generating textual descriptions of image data. In addition, attention-based image captioning mechanisms have also been proposed, which give state-ofthe- art results in image captioning. However, many applications and analyses of these methodologies have not been made in the case of languages from the Indian subcontinent. This paper presents attention-based merge architecture models to achieve accurate captions of images in four Indian languages- Marathi, Kannada, Malayalam, and Tamil. The widely known Flickr8K dataset was used for this project. Pre-trained Convolutional Neural Network (CNN) models and language decoder attention models were implemented, which serve as the components of the mergearchitecture proposed here. Finally, the accuracy of the generated captions was compared against the gold captions using Bilingual Evaluation Understudy (BLEU) as an evaluation metric. It was observed that the merge architectures consisting of InceptionV3 give the best results for the languages we test on, the scores discussed in the paper. Highest BLEU-1 scores obtained for each language were: 0.4939 for Marathi, 0.4557 for Kannada, 0.5082 for Malayalam, and 0.5201 for Tamil. Our proposed architectures gave much higher scores than other architectures implemented for these languages.
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Kong, Phutphalla, Matei Mancas, Bernard Gosselin, and Kimtho Po. "DeepRare: Generic Unsupervised Visual Attention Models." Electronics 11, no. 11 (2022): 1696. http://dx.doi.org/10.3390/electronics11111696.
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Visual attention selects data considered as “interesting” by humans, and it is modeled in the field of engineering by feature-engineered methods finding contrasted/surprising/unusual image data. Deep learning drastically improved the models efficiency on the main benchmark datasets. However, Deep Neural Networks-based (DNN-based) models are counterintuitive: surprising or unusual data are by definition difficult to learn because of their low occurrence probability. In reality, DNN-based models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this article, we propose a new family of visual attention models called DeepRare and especially DeepRare2021 (DR21), which uses the power of DNNs’ feature extraction and the genericity of feature-engineered algorithms. This algorithm is an evolution of a previous version called DeepRare2019 (DR19) based on this common framework. DR21 (1) does not need any additional training other than the default ImageNet training, (2) is fast even on CPU, (3) is tested on four very different eye-tracking datasets showing that DR21 is generic and is always within the top models on all datasets and metrics while no other model exhibits such a regularity and genericity. Finally, DR21 (4) is tested with several network architectures such as VGG16 (V16), VGG19 (V19), and MobileNetV2 (MN2), and (5) it provides explanation and transparency on which parts of the image are the most surprising at different levels despite the use of a DNN-based feature extractor.
Dissertations / Theses on the topic "Attention based models"
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Belkacem, Thiziri. "Neural models for information retrieval : towards asymmetry sensitive approaches based on attention models." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30167.
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Ce travail se situe dans le contexte de la recherche d'information (RI) utilisant des techniques d'intelligence artificielle (IA) telles que l'apprentissage profond (DL). Il s'intéresse à des tâches nécessitant l'appariement de textes, telles que la recherche ad-hoc, le domaine du questions-réponses et l'identification des paraphrases. L'objectif de cette thèse est de proposer de nouveaux modèles, utilisant les méthodes de DL, pour construire des modèles d'appariement basés sur la sémantique de textes, et permettant de pallier les problèmes de l'inadéquation du vocabulaire relatifs aux représentations par sac de mots, ou bag of words (BoW), utilisées dans les modèles classiques de RI. En effet, les méthodes classiques de comparaison de textes sont basées sur la représentation BoW qui considère un texte donné comme un ensemble de mots indépendants. Le processus d'appariement de deux séquences de texte repose sur l'appariement exact entre les mots. La principale limite de cette approche est l'inadéquation du vocabulaire. Ce problème apparaît lorsque les séquences de texte à apparier n'utilisent pas le même vocabulaire, même si leurs sujets sont liés. Par exemple, la requête peut contenir plusieurs mots qui ne sont pas nécessairement utilisés dans les documents de la collection, notamment dans les documents pertinents. Les représentations BoW ignorent plusieurs aspects, tels que la structure du texte et le contexte des mots. Ces caractéristiques sont très importantes et permettent de différencier deux textes utilisant les mêmes mots et dont les informations exprimées sont différentes. Un autre problème dans l'appariement de texte est lié à la longueur des documents. Les parties pertinentes peuvent être réparties de manières différentes dans les documents d'une collection. Ceci est d'autant vrai dans les documents volumineux qui ont tendance à couvrir un grand nombre de sujets et à inclure un vocabulaire variable. Un document long pourrait ainsi comporter plusieurs passages pertinents qu'un modèle d'appariement doit capturer. Contrairement aux documents longs, les documents courts sont susceptibles de concerner un sujet spécifique et ont tendance à contenir un vocabulaire plus restreint. L'évaluation de leur pertinence est en principe plus simple que celle des documents plus longs. Dans cette thèse, nous avons proposé différentes contributions répondant chacune à l'un des problèmes susmentionnés. Tout d'abord, afin de résoudre le problème d'inadéquation du vocabulaire, nous avons utilisé des représentations distribuées des mots (plongement lexical) pour permettre un appariement basé sur la sémantique entre les différents mots. Ces représentations ont été utilisées dans des applications de RI où la similarité document-requête est calculée en comparant tous les vecteurs de termes de la requête avec tous les vecteurs de termes du document, indifféremment. Contrairement aux modèles proposés dans l'état-de-l'art, nous avons étudié l'impact des termes de la requête concernant leur présence/absence dans un document. Nous avons adopté différentes stratégies d'appariement document/requête. L'intuition est que l'absence des termes de la requête dans les documents pertinents est en soi un aspect utile à prendre en compte dans le processus de comparaison. En effet, ces termes n'apparaissent pas dans les documents de la collection pour deux raisons possibles : soit leurs synonymes ont été utilisés ; soit ils ne font pas partie du contexte des documents en questions<br>This work is situated in the context of information retrieval (IR) using machine learning (ML) and deep learning (DL) techniques. It concerns different tasks requiring text matching, such as ad-hoc research, question answering and paraphrase identification. The objective of this thesis is to propose new approaches, using DL methods, to construct semantic-based models for text matching, and to overcome the problems of vocabulary mismatch related to the classical bag of word (BoW) representations used in traditional IR models. Indeed, traditional text matching methods are based on the BoW representation, which considers a given text as a set of independent words. The process of matching two sequences of text is based on the exact matching between words. The main limitation of this approach is related to the vocabulary mismatch. This problem occurs when the text sequences to be matched do not use the same vocabulary, even if their subjects are related. For example, the query may contain several words that are not necessarily used in the documents of the collection, including relevant documents. BoW representations ignore several aspects about a text sequence, such as the structure the context of words. These characteristics are important and make it possible to differentiate between two texts that use the same words but expressing different information. Another problem in text matching is related to the length of documents. The relevant parts can be distributed in different ways in the documents of a collection. This is especially true in large documents that tend to cover a large number of topics and include variable vocabulary. A long document could thus contain several relevant passages that a matching model must capture. Unlike long documents, short documents are likely to be relevant to a specific subject and tend to contain a more restricted vocabulary. Assessing their relevance is in principle simpler than assessing the one of longer documents. In this thesis, we have proposed different contributions, each addressing one of the above-mentioned issues. First, in order to solve the problem of vocabulary mismatch, we used distributed representations of words (word embedding) to allow a semantic matching between the different words. These representations have been used in IR applications where document/query similarity is computed by comparing all the term vectors of the query with all the term vectors of the document, regardless. Unlike the models proposed in the state-of-the-art, we studied the impact of query terms regarding their presence/absence in a document. We have adopted different document/query matching strategies. The intuition is that the absence of the query terms in the relevant documents is in itself a useful aspect to be taken into account in the matching process. Indeed, these terms do not appear in documents of the collection for two possible reasons: either their synonyms have been used or they are not part of the context of the considered documents. The methods we have proposed make it possible, on the one hand, to perform an inaccurate matching between the document and the query, and on the other hand, to evaluate the impact of the different terms of a query in the matching process. Although the use of word embedding allows semantic-based matching between different text sequences, these representations combined with classical matching models still consider the text as a list of independent elements (bag of vectors instead of bag of words). However, the structure of the text as well as the order of the words is important. Any change in the structure of the text and/or the order of words alters the information expressed. In order to solve this problem, neural models were used in text matching
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Saifullah, Mohammad. "Biologically-Based Interactive Neural Network Models for Visual Attention and Object Recognition." Doctoral thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79336.
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The main focus of this thesis is to develop biologically-based computational models for object recognition. A series of models for attention and object recognition were developed in the order of increasing functionality and complexity. These models are based on information processing in the primate brain, and specially inspired from the theory of visual information processing along the two parallel processing pathways of the primate visual cortex. To capture the true essence of incremental, constraint satisfaction style processing in the visual system, interactive neural networks were used for implementing our models. Results from eye-tracking studies on the relevant visual tasks, as well as our hypothesis regarding the information processing in the primate visual system, were implemented in the models and tested with simulations. As a first step, a model based on the ventral pathway was developed to recognize single objects. Through systematic testing, structural and algorithmic parameters of these models were fine tuned for performing their task optimally. In the second step, the model was extended by considering the dorsal pathway, which enables simulation of visual attention as an emergent phenomenon. The extended model was then investigated for visual search tasks. In the last step, we focussed on occluded and overlapped object recognition. A couple of eye-tracking studies were conducted in this regard and on the basis of the results we made some hypotheses regarding information processing in the primate visual system. The models were further advanced on the lines of the presented hypothesis, and simulated on the tasks of occluded and overlapped object recognition. On the basis of the results and analysis of our simulations we have further found that the generalization performance of interactive hierarchical networks improves with the addition of a small amount of Hebbian learning to an otherwise pure error-driven learning. We also concluded that the size of the receptive fields in our networks is an important parameter for the generalization task and depends on the object of interest in the image. Our results show that networks using hard coded feature extraction perform better than the networks that use Hebbian learning for developing feature detectors. We have successfully demonstrated the emergence of visual attention within an interactive network and also the role of context in the search task. Simulation results with occluded and overlapped objects support our extended interactive processing approach, which is a combination of the interactive and top-down approach, to the segmentation-recognition issue. Furthermore, the simulation behavior of our models is in line with known human behavior for similar tasks. In general, the work in this thesis will improve the understanding and performance of biologically-based interactive networks for object recognition and provide a biologically-plausible solution to recognition of occluded and overlapped objects. Moreover, our models provide some suggestions for the underlying neural mechanism and strategies behind biological object recognition.
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Borba, Gustavo Benvenutti. "Automatic extraction of regions of interest from images based on visual attention models." Universidade Tecnológica Federal do Paraná, 2010. http://repositorio.utfpr.edu.br/jspui/handle/1/1295.
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UOL; CAPES<br>Esta tese apresenta um método para a extração de regiões de interesse (ROIs) de imagens. No contexto deste trabalho, ROIs são definidas como os objetos semânticos que se destacam em uma imagem, podendo apresentar qualquer tamanho ou localização. O novo método baseia-se em modelos computacionais de atenção visual (VA), opera de forma completamente bottom-up, não supervisionada e não apresenta restrições com relação à categoria da imagem de entrada. Os elementos centrais da arquitetura são os modelos de VA propostos por Itti-Koch-Niebur e Stentiford. O modelo de Itti-Koch-Niebur considera as características de cor, intensidade e orientação da imagem e apresenta uma resposta na forma de coordenadas, correspondentes aos pontos de atenção (POAs) da imagem. O modelo Stentiford considera apenas as características de cor e apresenta a resposta na forma de áreas de atenção na imagem (AOAs). Na arquitetura proposta, a combinação de POAs e AOAs permite a obtenção dos contornos das ROIs. Duas implementações desta arquitetura, denominadas 'primeira versão' e 'versão melhorada' são apresentadas. A primeira versão utiliza principalmente operações tradicionais de morfologia matemática. Esta versão foi aplicada em dois sistemas de recuperação de imagens com base em regiões. No primeiro, as imagens são agrupadas de acordo com as ROIs, ao invés das características globais da imagem. O resultado são grupos de imagens mais significativos semanticamente, uma vez que o critério utilizado são os objetos da mesma categoria contidos nas imagens. No segundo sistema, á apresentada uma combinação da busca de imagens tradicional, baseada nas características globais da imagem, com a busca de imagens baseada em regiões. Ainda neste sistema, as buscas são especificadas através de mais de uma imagem exemplo. Na versão melhorada da arquitetura, os estágios principais são uma análise de coerência espacial entre as representações de ambos modelos de VA e uma representação multi-escala das AOAs. Se comparada à primeira versão, esta apresenta maior versatilidade, especialmente com relação aos tamanhos das ROIs presentes nas imagens. A versão melhorada foi avaliada diretamente, com uma ampla variedade de imagens diferentes bancos de imagens públicos, com padrões-ouro na forma de bounding boxes e de contornos reais dos objetos. As métricas utilizadas na avaliação foram presision, recall, F1 e area of overlap. Os resultados finais são excelentes, considerando-se a abordagem exclusivamente bottom-up e não-supervisionada do método.<br>This thesis presents a method for the extraction of regions of interest (ROIs) from images. By ROIs we mean the most prominent semantic objects in the images, of any size and located at any position in the image. The novel method is based on computational models of visual attention (VA), operates under a completely bottom-up and unsupervised way and does not present con-straints in the category of the input images. At the core of the architecture is de model VA proposed by Itti, Koch and Niebur and the one proposed by Stentiford. The first model takes into account color, intensity, and orientation features and provides coordinates corresponding to the points of attention (POAs) in the image. The second model considers color features and provides rough areas of attention (AOAs) in the image. In the proposed architecture, the POAs and AOAs are combined to establish the contours of the ROIs. Two implementations of this architecture are presented, namely 'first version' and 'improved version'. The first version mainly on traditional morphological operations and was applied in two novel region-based image retrieval systems. In the first one, images are clustered on the basis of the ROIs, instead of the global characteristics of the image. This provides a meaningful organization of the database images, since the output clusters tend to contain objects belonging to the same category. In the second system, we present a combination of the traditional global-based with region-based image retrieval under a multiple-example query scheme. In the improved version of the architecture, the main stages are a spatial coherence analysis between both VA models and a multiscale representation of the AOAs. Comparing to the first one, the improved version presents more versatility, mainly in terms of the size of the extracted ROIs. The improved version was directly evaluated for a wide variety of images from different publicly available databases, with ground truth in the form of bounding boxes and true object contours. The performance measures used were precision, recall, F1 and area overlap. Experimental results are of very high quality, particularly if one takes into account the bottom-up and unsupervised nature of the approach.
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Kliegl, Reinhold, Ping Wei, Michael Dambacher, Ming Yan, and Xiaolin Zhou. "Experimental effects and individual differences in linear mixed models: Estimating the relationship between spatial, object, and attraction effects in visual attention." Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5685/.
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Linear mixed models (LMMs) provide a still underused methodological perspective on combining experimental and individual-differences research. Here we illustrate this approach with two-rectangle cueing in visual attention (Egly et al., 1994). We replicated previous experimental cue-validity effects relating to a spatial shift of attention within an object (spatial effect), to attention switch between objects (object effect), and to the attraction of attention toward the display centroid (attraction effect), also taking into account the design-inherent imbalance of valid and other trials. We simultaneously estimated variance/covariance components of subject-related random effects for these spatial, object, and attraction effects in addition to their mean reaction times (RTs). The spatial effect showed a strong positive correlation with mean RT and a strong negative correlation with the attraction effect. The analysis of individual differences suggests that slow subjects engage attention more strongly at the cued location than fast subjects. We compare this joint LMM analysis of experimental effects and associated subject-related variances and correlations with two frequently used alternative statistical procedures
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Dimitriadis, Spyridon. "Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177186.
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With the recent advantages of machine learning in cheminformatics, the drug discovery process has been accelerated; providing a high impact in the field of medicine and public health. Molecular property and activity prediction are key elements in the early stages of drug discovery by helping prioritize the experiments and reduce the experimental work. In this thesis, a novel approach for multi-task regression using a text-based Transformer model is introduced and thoroughly explored for training on a number of properties or activities simultaneously. This multi-task regression with Transformer based model is inspired by the field of Natural Language Processing (NLP) which uses prefix tokens to distinguish between each task. In order to investigate our architecture two data categories are used; 133 biological activities from ExCAPE database and three physical chemistry properties from MoleculeNet benchmark datasets. The Transformer model consists of the embedding layer with positional encoding, a number of encoder layers, and a Feedforward Neural Network (FNN) to turn it into a regression problem. The molecules are represented as a string of characters using the Simplified Molecular-Input Line-Entry System (SMILES) which is a ’chemistry language’ with its own syntax. In addition, the effect of Transfer Learning is explored by experimenting with two pretrained Transformer models, pretrained on 1.5 million and on 100 million molecules. The text-base Transformer models are compared with a feature-based Support Vector Regression (SVR) with the Tanimoto kernel where the input molecules are encoded as Extended Connectivity Fingerprint (ECFP), which are calculated features. The results have shown that Transfer Learning is crucial for improving the performance on both property and activity predictions. On bioactivity tasks, the larger pretrained Transformer on 100 million molecules achieved comparable performance to the feature-based SVR model; however, overall SVR performed better on the majority of the bioactivity tasks. On the other hand, on physicochemistry property tasks, the larger pretrained Transformer outperformed SVR on all three tasks. Concluding, the multi-task regression architecture with the prefix token had comparable performance with the traditional feature-based approach on predicting different molecular properties or activities. Lastly, using the larger pretrained models trained on a wide chemical space can play a key role in improving the performance of Transformer models on these tasks.
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Holmström, Oskar. "Exploring Transformer-Based Contextual Knowledge Graph Embeddings : How the Design of the Attention Mask and the Input Structure Affect Learning in Transformer Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175400.
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The availability and use of knowledge graphs have become commonplace as a compact storage of information and for lookup of facts. However, the discrete representation makes the knowledge graph unavailable for tasks that need a continuous representation, such as predicting relationships between entities, where the most probable relationship needs to be found. The need for a continuous representation has spurred the development of knowledge graph embeddings. The idea is to position the entities of the graph relative to each other in a continuous low-dimensional vector space, so that their relationships are preserved, and ideally leading to clusters of entities with similar characteristics. Several methods to produce knowledge graph embeddings have been created, from simple models that minimize the distance between related entities to complex neural models. Almost all of these embedding methods attempt to create an accurate static representation of each entity and relation. However, as with words in natural language, both entities and relations in a knowledge graph hold different meanings in different local contexts. With the recent development of Transformer models, and their success in creating contextual representations of natural language, work has been done to apply them to graphs. Initial results show great promise, but there are significant differences in archi- tecture design across papers. There is no clear direction on how Transformer models can be best applied to create contextual knowledge graph embeddings. Two of the main differences in previous work is how the attention mask is applied in the model and what input graph structures the model is trained on. This report explores how different attention masking methods and graph inputs affect a Transformer model (in this report, BERT) on a link prediction task for triples. Models are trained with five different attention masking methods, which to varying degrees restrict attention, and on three different input graph structures (triples, paths, and interconnected triples). The results indicate that a Transformer model trained with a masked language model objective has the strongest performance on the link prediction task when there are no restrictions on how attention is directed, and when it is trained on graph structures that are sequential. This is similar to how models like BERT learn sentence structure after being exposed to a large number of training samples. For more complex graph structures it is beneficial to encode information of the graph structure through how the attention mask is applied. There also seems to be some indications that the input graph structure affects the models’ capabilities to learn underlying characteristics in the knowledge graph that is trained upon.
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Klamser, Pascal. "Collective Information Processing and Criticality, Evolution and Limited Attention." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/23099.
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Im ersten Teil analysiere ich die Selbstorganisation zur Kritikalität (hier ein Phasenübergang von Ordnung zu Unordnung) und untersuche, ob Evolution ein möglicher Organisationsmechanismus ist.
Die Kernfrage ist, ob sich ein simulierter kohäsiver Schwarm, der versucht, einem Raubtier auszuweichen, durch Evolution selbst zum kritischen Punkt entwickelt, um das Ausweichen zu optimieren?
Es stellt sich heraus, dass (i) die Gruppe den Jäger am besten am kritischen Punkt vermeidet, aber (ii) nicht durch einer verstärkten Reaktion, sondern durch strukturelle Veränderungen, (iii) das Gruppenoptimum ist evolutionär unstabiler aufgrund einer maximalen räumlichen Selbstsortierung der Individuen.
Im zweiten Teil modelliere ich experimentell beobachtete Unterschiede im kollektiven Verhalten von Fischgruppen, die über mehrere Generationen verschiedenen Arten von größenabhängiger Selektion ausgesetzt waren.
Diese Größenselektion soll Freizeitfischerei (kleine Fische werden freigelassen, große werden konsumiert) und die kommerzielle Fischerei mit großen Netzbreiten (kleine/junge Individuen können entkommen) nachahmen.
Die zeigt sich, dass das Fangen großer Fische den Zusammenhalt und die Risikobereitschaft der Individuen reduziert.
Beide Befunde lassen sich mechanistisch durch einen Aufmerksamkeits-Kompromiss zwischen Sozial- und Umweltinformationen erklären.
Im letzten Teil der Arbeit quantifiziere ich die kollektive Informationsverarbeitung im Feld.
Das Studiensystem ist eine an sulfidische Wasserbedingungen angepasste Fischart mit einem kollektiven Fluchtverhalten vor Vögeln (wiederholte kollektive Fluchttauchgängen).
Die Fische sind etwa 2 Zentimeter groß, aber die kollektive Welle breitet sich über Meter in dichten Schwärmen an der Oberfläche aus.
Es zeigt sich, dass die Wellengeschwindigkeit schwach mit der Polarisation zunimmt, bei einer optimalen Dichte am schnellsten ist und von ihrer Richtung relativ zur Schwarmorientierung abhängt.<br>In the first part, I focus on the self-organization to criticality (here an order-disorder phase transition) and investigate if evolution is a possible self-tuning mechanism.
Does a simulated cohesive swarm that tries to avoid a pursuing predator self-tunes itself by evolution to the critical point to optimize avoidance?
It turns out that (i) the best group avoidance is at criticality but (ii) not due to an enhanced response but because of structural changes (fundamentally linked to criticality), (iii) the group optimum is not an evolutionary stable state, in fact (iv) it is an evolutionary accelerator due to a maximal spatial self-sorting of individuals causing spatial selection.
In the second part, I model experimentally observed differences in collective behavior of fish groups subject to multiple generation of different types of size-dependent selection.
The real world analog to this experimental evolution is recreational fishery (small fish are released, large are consumed) and commercial fishing with large net widths (small/young individuals can escape).
The results suggest that large harvesting reduces cohesion and risk taking of individuals.
I show that both findings can be mechanistically explained based on an attention trade-off between social and environmental information.
Furthermore, I numerically analyze how differently size-harvested groups perform in a natural predator and fishing scenario.
In the last part of the thesis, I quantify the collective information processing in the field.
The study system is a fish species adapted to sulfidic water conditions with a collective escape behavior from aerial predators which manifests in repeated collective escape dives.
These fish measure about 2 centimeters, but the collective wave spreads across meters in dense shoals at the surface.
I find that wave speed increases weakly with polarization, is fastest at an optimal density and depends on its direction relative to shoal orientation.
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Wennerholm, Pia. "The Role of High-Level Reasoning and Rule-Based Representations in the Inverse Base-Rate Effect." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Universitetsbiblioteket [distributör], 2001. http://publications.uu.se/theses/91-554-5178-0/.
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Desai, Anver. "Policy agenda-setting and the use of analytical agenda-setting models for school sport and physical education in South Africa." Thesis, University of the Western Cape, 2011. http://hdl.handle.net/11394/3596.
Full textAbstract:
This study focused on policy agenda-setting models for school sport and physical education in South Africa. The primary objective was to assess and propose options for improved agenda-setting by focussing on the use of agenda-setting models and by applying it to physical education and school sport and the policy agenda of the national government. The study has shown that pertinent school sport and physical education policy issues, as supported by key role-players and principal actors, were initially not placed on the formal policy agenda of government during the research investigation period (2005-2009). However, during 2010 and 2011 the issue of school sport and physical education received prominent attention by authorities and these developments were subsequently included in the study. The study aimed at contributing to existing policy agenda-setting models and by recommending changes to the Generic Process Model.The study also made a contribution by informing various role-players and stakeholders in education and school sport on the opportunities in policy agenda-setting. The study showed that policy agenda-setting is a vital step in the Generic Policy Process Model. Policy agendasetting in South Africa is critical, as it is important to place new and emerging policy issues on the policy agenda and as a participative public policy process is relatively new in this young democracy. The reader should not confuse the study as one dealing with school sport and physical education primarily, but rather as a research investigation dealing with policy agenda-setting models as applied to school sport and physical education.The secondary objectives of the study included the development of a historical perspective on trends and tendencies in education and sport in South Africa. A second objective was to provide theoretical perspectives on public policy and specifically on policy agenda-setting. From these theoretical perspectives, the Generic Policy Process Model was selected to use as a model that provided guidance on the overall policy process normally followed in South Africa. The Issue Attention Cycle and Principal Actor Models on Agenda-Setting were selected to apply to the case study to specifically ascertain important factors related to policy agenda-setting such as the identification of key role players as well as key policy issues. The Generic Policy Process Model provided for both a comprehensive set of phases as well as specific requirements and key issues to be addressed during each phase of the policy process.In terms of findings the study found that a number of specific agenda-setting elements or phases needed to be added to the Generic Policy Process Model, which includes a problem stage, triggers, initiator, issue creation and actors or policy stakeholders.The Principal Actor Model to agenda-setting was selected for application to the case as different actors have different levels of success at each policy stage. In the South African experience it is important to look at who sets the policy agenda and why, who can initiate agenda-setting and the role played by these principal actors in the agenda-setting process.Issue emergence often places policy issues on the policy agenda. The public is initially involved in issues, but in the long term public interest declines. The government realizes the significant costs involved in placing policy issues back on the agenda. This leads to a decline in issue attention by policy-makers and the public. The Issue Attention Cycle Model of
agenda setting was used to analyse this phenomenon in South African Education policy.The study provides a case assessment of the South African experience. From the research findings, a set of conclusions and recommendations were developed for improved policy agenda-setting models and implications for school sport and physical education, as well as tools to place it on the national policy agenda were identified. The research findings suggest that pertinent school sport and physical education policy issues, as supported by key roleplayers,stakeholders and principal actors were not placed on the formal policy agenda of the government as a vital step in the policy process between 2005 and 2009. Ever since, principal policy actors, civil society NGOs, and government officials placed sufficient pressure on the Minister of Basic Education to place Physical Education on the agenda. Subsequently,Minister Angie Motshega has placed physical education in the school Curriculum under the subject Life Orientation and Lifeskills. It has become evident from the research that agendasetting is both necessary to, and a complex phase in, the policy-making process.This study has shown that major policy issues such as physical education and school sport were neglected during the period 2005 and 2009 despite reformed and advanced policy cycles in government. It has also shown that the role of policy agenda-setting in the overall policymaking
process was revisited by government in the subsequent period 2010/2011 and placed on the policy agenda. Specific lessons of experience emanated from this process.The study recommends that the triggers of the agenda-setting phases be added to the Generic Policy Process Model, which should include the problem stage, triggers, initiators, issue creation, actors and policy stakeholders. Principal actors in the agenda-setting model in South Africa want the issue of physical education and school sport to be part of the school curriculum, and therefore be placed back on the policy agenda by the Government on its institutional agenda. Furthermore, the study showed that actors wanted it to be compulsory in all phases of the school (Foundation, Intermediate, Senior, GET, FET) and that it should have the same legal status as other subjects.The important findings include that: Comprehensive policy process models such as that of Dunn, Wissink and the Generic Process model may need to be reviewed to incorporate more fully the policy-agenda
setting stages of the overall process; Current policy agenda setting models in use are relevant and valuable in identifying key role players as well as key issues and considerations regarding the policy process; Institutional arrangements to strengthen the role of NGOs and lower level institutions,such as schools to participate in policy agenda setting are important; and the study has shown that a number of key factors have been identified that had a key influence on policy agenda-setting in the case of physical education and school sport in South Africa. These included the influence of changing political leadership, the competency of policy capacities in government, the profile of issues in the media etc. The key findings of the study have shown that further potential exists to improve
monitoring and evaluation and policy analysis.The study made a set of recommendations to principal actors such as the Minister of Education, Minister of Sport and Recreation, non-governmental organisations, interest groups,department officials and pressure groups. A set of research topics was also identified
for future research.<br>Philosophiae Doctor - PhD
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Garagnani, Max. "Understanding language and attention : brain-based model and neurophysiological experiments." Thesis, University of Cambridge, 2009. https://www.repository.cam.ac.uk/handle/1810/243852.
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This work concerns the investigation of the neuronal mechanisms at the basis of language acquisition and processing, and the complex interactions of language and attention processes in the human brain. In particular, this research was motivated by two sets of existing neurophysiological data which cannot be reconciled on the basis of current psycholinguistic accounts: on the one hand, the N400, a robust index of lexico-semantic processing which emerges at around 400ms after stimulus onset in attention demanding tasks and is larger for senseless materials (meaningless pseudowords) than for matched meaningful stimuli (words); on the other, the more recent results on the Mismatch Negativity (MMN, latency 100-250ms), an early automatic brain response elicited under distraction which is larger to words than to pseudowords. We asked what the mechanisms underlying these differential neurophysiological responses may be, and whether attention and language processes could interact so as to produce the observed brain responses, having opposite magnitude and different latencies. We also asked questions about the functional nature and anatomical characteristics of the cortical representation of linguistic elements. These questions were addressed by combining neurocomputational techniques and neuroimaging (magneto-encephalography, MEG) experimental methods. Firstly, a neurobiologically realistic neural-network model composed of neuron-like elements (graded response units) was implemented, which closely replicates the neuroanatomical and connectivity features of the main areas of the left perisylvian cortex involved in spoken language processing (i.e., the areas controlling speech output – left inferior-prefrontal cortex, including Broca’s area – and the main sensory input – auditory – areas, located in the left superior-temporal lobe, including Wernicke’s area). Secondly, the model was used to simulate early word acquisition processes by means of a Hebbian correlation learning rule (which reflects known synaptic plasticity mechanisms of the neocortex). The network was “taught” to associate pairs of auditory and articulatory activation patterns, simulating activity due to perception and production of the same speech sound: as a result, neuronal word representations distributed over the different cortical areas of the model emerged. Thirdly, the network was stimulated, in its “auditory cortex”, with either one of the words it had learned, or new, unfamiliar pseudoword patterns, while the availability of attentional resources was modulated by changing the level of non-specific, global cortical inhibition. In this way, the model was able to replicate both the MMN and N400 brain responses by means of a single set of neuroscientifically grounded principles, providing the first mechanistic account, at the cortical-circuit level, for these data. Finally, in order to verify the neurophysiological validity of the model, its crucial predictions were tested in a novel MEG experiment investigating how attention processes modulate event-related brain responses to speech stimuli. Neurophysiological responses to the same words and pseudowords were recorded while the same subjects were asked to attend to the spoken input or ignore it. The experimental results confirmed the model’s predictions; in particular, profound variability of magnetic brain responses to pseudowords but relative stability of activation to words as a function of attention emerged. While the results of the simulations demonstrated that distributed cortical representations for words can spontaneously emerge in the cortex as a result of neuroanatomical structure and synaptic plasticity, the experimental results confirm the validity of the model and provide evidence in support of the existence of such memory circuits in the brain. This work is a first step towards a mechanistic account of cognition in which the basic atoms of cognitive processing (e.g., words, objects, faces) are represented in the brain as discrete and distributed action-perception networks that behave as closed, independent systems.
Books on the topic "Attention based models"
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Sokol'skaya, Elena, and Boris Kochurov. Geoecology of the city: models of environmental quality. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1205961.
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The monograph examines the features of studying the geoecological state of urbanized territories, reveals the use of integrated assessment and mapping in urban diagnostics, and finds a solution to geoecological problems on the example of world cities that are leading in the rating for quality of life.
 The components of an information and analytical model of the urban environment for assessing the geoecological situation are described; an algorithm for a comprehensive study of the geoecological state aimed at an adequate assessment of the quality of the urban environment. Special attention is paid to the methodology of geoecological assessment of the quality of the urban environment based on multifactor modeling, which allows making recommendations for improving the comfort of living of the population.
 It is intended for a wide range of specialists in the field of geoecology of the city, and can also be used as a textbook for students of environmental, natural-geographical, engineering specialties.
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Odincov, Boris. Models and intelligent systems. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1060845.
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The monograph consists of three chapters, the first of which outlines the theoretical foundations of intelligent information systems. Special attention is paid to the disclosure of the term "model" as the intended meaning depends on the understanding of the material. Introduces and examines the new concepts such as the associative and intuitive knowledge while in the creation of intellectual information systems are not used. 
 The second Chapter contains the analysis of problems of development of artificial intelligence (AI), developed in two directions: classical and statistical. Discusses difficulties in the development of the classical approach, associated with identifying the meaning of words, phrases, text, and formulating thoughts. The analysis of problems arising in the play of imagination and insight, machine understanding of natural language texts, play, verbalization and reflection. 
 The third Chapter contains examples of the development of intelligent information systems and technologies in practice of management of economic objects. Theoretical bases of construction of information robots designed to support the task hierarchy of the knowledge base and generating control regulations. The technology of their creation and application in the management of the business efficiency of enterprise business processes and its investment activities. 
 Focused on researchers and developers, AI and intelligent information systems, as well as graduate students and faculty in related academic disciplines.
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Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.
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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation.
 
 It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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Gruzdeva, Viktoriya, Georgiy Gruzdev, Yuliya Klyueva, and Vlada Konova. Development of the service sector based on a customer-oriented approach. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1977989.
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In the monograph, the use of the civilizational paradigm of the analysis of economic processes and phenomena allows us to consider the specifics of the functioning of the hospitality industry in the conditions of the formation of the information economy. The features of the client-oriented approach in its institutional and dynamic dimensions are revealed. Special attention is paid to inclusive technologies in the field of hospitality.
 The result of the analysis of the features of the formation of the institutional model of the hotel business in the Nizhny Novgorod region is presented. On the basis of a systematic approach, the entire process of functioning of a hospitality enterprise is considered from the standpoint of structural and functional dynamics. Special attention is paid to the analysis of the formation of the corporate culture of hotel and restaurant complexes in modern conditions.
 It is intended for researchers, teachers, students studying in the areas of higher education 43.03.01 "Service", 43.03.02 "Tourism" and 43.03.03 "Hotel business", as well as for anyone interested in the problems of the development of the service sector in the conditions of the formation of information and cybernetic civilization.
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Tarakanov, Andrey, A. Sumin, and A. Hvostov. Mathematical problems of decision-making in dynamic organizational systems. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1871445.
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The monograph develops the theory of decision-making in dynamic organizational systems with a complex structure in conditions of conflict and uncertainty. An overview of the current state of the theory is given. The systems are studied: hierarchical, coalition and coalition-hierarchical (hybrid). The main attention in the process of constructing mathematical models of systems is paid to the description of ways of information interaction of decision makers. At the same time, the variants of their unfavorable (conflict) and benevolent "attitude" to each other are taken into account. Two approaches to decision-making based on the principle of guaranteed results and approaches of game theory are proposed. Exactly: 1) making decisions from the point of view of a dedicated participant in the system based on penalty functions and obtaining the necessary optimality conditions; 2) making decisions based on special optimality principles constructed using the principles of Nash, Pareto, Joffrion, Stackelberg, Slater, threats — counter-threats and obtaining sufficient optimality conditions. Some theoretical results are illustrated by model examples.
 For researchers, postgraduates and students dealing with theoretical and practical issues of decision-making in complex systems.
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Astaf'eva, Ol'ga, Natal'ya Moiseenko, Aleksandr Kozlovskiy, Tat'yana Shemyakina, and Viktor Serov. Risk management in construction. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1842952.
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The monograph is devoted to the issues of risk management in the organizations of the investment and construction complex. The issues of risk classification are consistently considered, approaches to determining the types and types of risks are established. Attention is paid to approaches to the construction of a risk management mechanism and the specifics of the impact on the identified risks in terms of minimizing possible damage. The issues of state regulation are highlighted, a complex economic problem related to the study of the effectiveness of the chosen strategy of real investment projects based on the use of various methods and models of risk analysis is considered. Modern educational and methodological materials tested in the practice of enterprises and organizations of the construction complex of Moscow and the Moscow region were used.
 For a wide range of readers interested in the issues of risk management in construction. It will be useful for students, postgraduates and teachers of economic universities.
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Vavrenyuk, Aleksandr, Viktor Makarov, and Stanislav Kutepov. Operating systems. UNIX bases. INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/11186.
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In the manual basics command interfey-are covered
 са operating systems of UNIX family. Much attention is paid
 to practical use of teams of system and opportunities of language
 programming, shell provided by a cover. In a grant vklyu-
 Chena also some sections devoted to bases administrirova-
 niya and to network means of OS. At the end of each section there are questions
 for self-checking, the appendix contains a large number at -
 mayors of writing of shell-procedures.
 The manual is addressed to the students studying the modern
 information technologies according to programs of a bachelor degree, and also all,
 who wants to master the OS command interface of family independently
 UNIX in the shortest possible time.
 The edition can also be used as the short reference book on wasps -
 new UNIX OS.
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Rubtsov, Nickolai, Mikhail Alymov, Alexander Kalinin, Alexey Vinogradov, Alexey Rodionov, and Kirill Troshin. Remote studies of combustion and explosion processes based on optoelectronic methods. AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62876066a124d8.04785158.
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The main objective of this book is to acquaint the reader with the main modern problems of
 the multisensor data analysis and opportunities of the hyperspectral shooting being carried out in the
 wide range of wavelengths from ultraviolet to the infrared range, visualization of the fast combustion
 processes of flame propagation and flame acceleration, the limit phenomena at flame ignition and
 propagation. The book can be useful to students of the high courses and scientists dealing with problems
 of optical spectroscopy, vizualisation, digital recognizing images and gaseous combustion.
 The main goal of this book is to bring to the attention of the reader the main modern
 problems of multisensory data analysis and the possibilities of hyperspectral imaging, carried out
 in a broad wave-length range from ultraviolet to infrared by methods of visualizing fast combustion
 processes, propagation and flames acceleration, and limiting phenomena during ignition and flame
 propagation. The book can be useful for students of higher courses and experimental scientists dealing
 with problems of optical spectroscopy, visualization, pattern recognition and gas combustion.
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Astrahancevaa, Irina, Sergey Bobkov, Vadim Mizonov, and Sergey Boykov. Modeling of systems. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1831624.
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The textbook discusses general issues of system modeling, analytical, empirical and simulation approaches to modeling. Typical mathematical schemes used in the analytical approach, methods and tools of simulation modeling of systems are given. Attention is also paid to network and agent-based alternative approaches to modeling.
 Meets the requirements of the federal state educational standards of higher education of the latest generation.
 It is intended for undergraduate students studying in the direction of 09.03.02 "Information systems and technologies", whose working curricula include the discipline "Systems Modeling". It is assumed that the training plans also include training courses "Discrete Mathematics", "Mathematical logic and theory of algorithms", "System analysis". The manual will also be useful for master's degree students studying in the direction 09.04.02 "Information systems and technologies" (discipline "Models of information processes and systems").
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Naumov, Vladimir. Consumer behavior. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014653.
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The book describes the basic issues concerning consumer behavior on the basis of the simulation of the decision-making process on buying behavior of customers in the sales area of the store and shopping Internet sites. 
 The classification of models of consumer behavior, based on research in the area of economic, social and psychological theories and empirical evidence regarding decision-making by consumers when purchasing the goods, including online stores. Methods of qualitative and quantitative research of consumer behavior, fundamentals of statistical processing of empirical data. 
 Attention is paid to the processes of consumers ' perception of brands (brands) and advertising messages, the basic rules for the display of goods (merchandising) and its impact on consumer decision, recommendations on the use of psychology of consumer behavior in personal sales.
 Presents an integrated model of consumer behavior in the Internet environment, the process of perception of the visitor of the company, the factors influencing consumer choice of goods online. 
 Is intended for preparation of bachelors in directions of preparation 38.03.02 "Management", 38.03.06 "trading business" and can be used for training of bachelors in direction of training 43.03.01 "Service", and will also be useful for professionals working in the field of marketing, distribution and sales.
Book chapters on the topic "Attention based models"
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Elkelany, Amany, Robert Ross, and Susan Mckeever. "WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_10.
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AbstractRecently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments.
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Hommel, Sebastian, Ahmad Rabie, and Uwe Handmann. "Attention and Emotion Based Adaption of Dialog Systems." In Intelligent Systems: Models and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33959-2_12.
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Aechtner, Jonathan, Anna Wilbik, and Mirela Popa. "Assessing Aggressive Driving Behaviour Using Attention Based Models." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-74650-5_4.
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Milanova, Mariofanna, and Engin Mendi. "Attention in Image Sequences: Biology, Computational Models, and Applications." In Advances in Reasoning-Based Image Processing Intelligent Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24693-7_6.
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Roussinov, Dmitri, Serge Sharoff, and Nadezhda Puchnina. "Recognizing Semantic Relations: Attention-Based Transformers vs. Recurrent Models." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45439-5_37.
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Kazi, Anees, Shadi Albarqouni, Amelia Jimenez Sanchez, et al. "Automatic Classification of Proximal Femur Fractures Based on Attention Models." In Machine Learning in Medical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67389-9_9.
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Xiao, Yao, Haoxin Ruan, Xujian Zhao, Peiquan Jin, and Xuebo Cai. "Music Emotion Recognition Using Multi-head Self-attention-Based Models." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4752-2_9.
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Gajbhiye, Amit, Thomas Winterbottom, Noura Al Moubayed, and Steven Bradley. "Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models." In Artificial Neural Networks and Machine Learning – ICANN 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61609-0_50.
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Caffaro, Fabio, Lorenzo Bongiovanni, and Claudio Rossi. "Geo-temporal Crime Forecasting Using a Deep Learning Attention-Based Model." In Security Informatics and Law Enforcement. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62083-6_26.
Full textAbstract:
AbstractCrime prediction is a crucial problem in law enforcement, and the ability to forecast where and when crimes are likely to occur can help police departments allocate their resources effectively and prevent crimes. In this chapter, we propose a geo-temporal crime forecasting model based on a transformer architecture. We use a public dataset from the Boston Police Department and forecast crimes in each cell of a 1 km × 1 km grid. We use an encoder–decoder structure to capture the spatiotemporal patterns of the crimes. The encoder elaborates the crimes that occurred in each cell during the previous n days, and the decoder generates predictions of future crimes in each cell for the next m days. Our model considers both spatial and temporal correlations, which is challenging for traditional models. We evaluate the model on the Boston crime dataset and compare it with traditional solutions. Our experiments show that our model outperforms traditional models, achieving better accuracy in crime prediction. Overall, our proposed geo-temporal crime forecasting model is a promising approach for predicting crime in a given area.
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Li, Caizi, Qianqian Tong, Xiangyun Liao, et al. "Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation." In Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12029-0_28.
Full textConference papers on the topic "Attention based models"
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Wang, Junpeng, Chin-Chia Michael Yeh, Uday Singh Saini, and Mahashweta Das. "Visual Attention Exploration in Vision-Based Mamba Models." In 2025 IEEE 18th Pacific Visualization Conference (PacificVis). IEEE, 2025. https://doi.org/10.1109/pacificvis64226.2025.00031.
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Wu, Yaning, Nathalie Japkowicz, Sebastien Gilbert, and Roberto Corizzo. "Attention-Based Medical Knowledge Injection in Deep Image Classification Models." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651246.
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Maddineni, Vinod Kumar, Naga Babu Koganti, and Praveen Damacharla. "Enhancing Microgrid Performance Prediction with Attention-Based Deep Learning Models." In 2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE, 2024. https://doi.org/10.1109/icitacee62763.2024.10762767.
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Zhao, Peng, Ruicong Wang, Zijie Lin, Zexu Pan, Haizhou Li, and Xueyi Zhang. "Ensemble Deep Learning Models for EEG-Based Auditory Attention Decoding." In 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024. https://doi.org/10.1109/iscslp63861.2024.10800199.
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Verlekar, Tanmay, and Prateek Upadhya. "Shallow convolution and attention-based models for micro-expression recognition." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-68.
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Yun, Gwang-Hyeon, and Young-Rae Cho. "Comparative Analysis of Attention-based Models for Drug-Target Interaction Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822678.
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Disabato, Raffaele, AprilPyone MaungMaung, Huy H. Nguyen, and Isao Echizen. "Transfer-Based Adversarial Attack Against Multimodal Models by Exploiting Perturbed Attention Region." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10849236.
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Anubhove, Md Sadik Tasrif, S. M. Masum Ahmed, Mohammad Zeyad, and Asmay Ajma Khanam Aka. "Wind Speed Forecasting for Wind Turbines Using Attention-Based Machine Learning Models." In 2024 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2024. https://doi.org/10.1109/wiecon-ece64149.2024.10915017.
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Gueriani, Afrah, Hamza Kheddar, and Ahmed Cherif Mazari. "Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models." In 2024 International Conference on Telecommunications and Intelligent Systems (ICTIS). IEEE, 2024. https://doi.org/10.1109/ictis62692.2024.10894509.
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Maftoun, Mohammad, Amir Mohammad Ranjbar, Hamidreza Ghavitan, and Maryam Khademi. "Attention-Based Deep Learning Models for Fraud Detection in Imbalanced Transaction Datasets." In 2025 11th International Conference on Web Research (ICWR). IEEE, 2025. https://doi.org/10.1109/icwr65219.2025.11006225.
Full textReports on the topic "Attention based models"
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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2211.
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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.
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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-making, emphasizing the trade-offs between sample efficiency, generalization, and computational complexity. Special attention is given to uncertainty quantification, explainability techniques, and ethical AI, ensuring AI models remain transparent, accountable, and risk-aware. By integrating probabilistic reasoning, deep learning, and structured decision models, this research highlights how AI can make reliable, interpretable, and human-aligned decisions in complex, high-stakes environments. The findings underscore the importance of hybrid AI frameworks, explainable probabilistic models, and uncertainty-aware optimization, shaping the future of trustworthy, scalable, and ethically responsible AI-driven decision-making. Keywords Decision theory, model-based AI, probabilistic learning, Bayesian inference, probabilistic graphical models, reinforcement learning, Markov decision processes, uncertainty quantification, explainable AI, causal inference, model-free learning, Monte Carlo methods, variational inference, hybrid AI frameworks, ethical AI, risk-aware decision-making, optimal control, trust in AI, interpretable machine learning, autonomous systems, financial AI, healthcare AI, AI governance, explainability techniques, real-world AI applications.
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Leis and Walsh. L51575 Mechanics-Based Analysis of SCC in a Carbonate-Bicarbonate Environment. Pipeline Research Council International, Inc. (PRCI), 1988. http://dx.doi.org/10.55274/r0010306.
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Stress-corrosion cracking (SCC) occurs occasionally in line-pipe steels. Reviews of this cracking indicate that the environment is carbonate-bicarbonate and that the cracking is primarily intergranular. Research sponsored by the PRCI Line Pipe Research Supervisory Committee (LPRSC) has over the years identified metallurgical and electrochemical parameters as playing a role in the process. This work has also argued the significance of strain rate and its relationship to the service loading, given that dissolution is indicated as the controlling mechanism. While much has been learned about the mechanism of cracking, very little has been learned about how to directly relate the nucleation and growth of cracks to the loading, the metallurgy, and the environmental parameters. The continual development of new line-pipe steels, the probable development of reliable in-line inspection (ILI) tools to detect SCC, and the occasional discovery of cracking colonies during field surveys have recently centered attention on methods to rank line-pipe resistance to SCC and characterize crack-growth rates. Ranking line-pipe resistance to SCC may be done in terms of a threshold stress for nucleation of SCC or the rate of crack growth at some crack depth beyond nucleation. Estimating remaining life of cracks located by an ILI tool or confirmed in a field survey involves only crack growth rate data or data that define a threshold stress for continued growth. Recent attention of the SCC subgroup of the LPRSC, therefore, has focussed on experimental protocols to assess susceptibility, determine thresholds, and establish growth rate behavior. The tapered-tension test (TTT) has been developed to determine stress thresholds for crack nucleation, whereas several different prenotched or precracked geometries have been or are now being used to assess crack growth. Attention has also focussed on modelling SCC thresholds and crack growth behavior so that data developed under laboratory conditions can be adapted to assess field cracking situations. Models are being explored for both threshold and crack-growth conditions. This report presents the development and validation for a model that is being adapted to line-pipe problems for the SCC subgroup. This model deals with the threshold for and the growth of SCC cracks up to about 0.020-inch deep.
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Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Banco de España, 2023. http://dx.doi.org/10.53479/33412.
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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses from 2016 to 2022. We do this using traditional techniques, such as dictionary-based methods, and two large language models (LLMs), namely Bert and ChatGPT, concluding that LLMs better reflect the stance identified by human experts. In particular, we observe that ChatGPT exhibits a higher degree of alignment because it can capture subtler information than BERT. Our study suggests that LLMs are an effective tool to improve sentiment measurements for policy-specific texts, though they are not infallible and may be subject to new risks, like higher sensitivity to the length of texts, and prompt engineering.
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Keeney, Roman, and Thomas Hertel. GTAP-AGR: A Framework for Assessing the Implications of Multilateral Changes in Agricultural Policies. GTAP Technical Paper, 2005. http://dx.doi.org/10.21642/gtap.tp24.
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Global models of agricultural trade have a long and distinguished history. The introduction of the GTAP data base and modeling project represented a significant advance forward as it put modelers and trade policy analysts on common ground. After an initial generation of GTAP based modeling of agricultural trade policy using the standard modeling framework, individual researchers have begun introducing agricultural specificity into the standard modeling framework in order to better capture the particular features of the agricultural economy pertinent to their research questions. This technical paper follows in that same tradition by reviewing important linkages between international trade and the farm and food economy and introducing them into the standard GTAP modeling framework, offering a special purpose version of the model nicknamed GTAP-AGR. We introduce this agricultural specificity by introducing new behavioral relationships into the standard GTAP framework. We focus particular attention on the factor markets, which play a critical role in determining the incidence of producer subsidies. This includes modifying both the factor supply and derived demand equations. We also modify the specification of consumer demand, assuming separability of food from non-food commodities. Finally, we introduce the important substitution possibilities amongst feedstuffs used in the livestock sector. Where possible we support these new behavioral relationships with literature-based estimates of both the mean and standard deviation of behavioral parameters. The express purpose of this is to support systematic sensitivity analysis with respect to policy reform scenarios performed with GTAP-AGR. In addition to documenting these extensions to the standard modeling framework, the paper has an additional goal, and that is to gauge the performance of the GTAP-AGR model and how it differs from the standard GTAP framework. We do this primarily by comparing the farm level supply and demand response in terms of policy incidence for the two frameworks. In addition, we evaluate the ability of both models to reproduce observed price volatility in an effort to validate the performance of these models. Finally, we evaluate the results of the two models in a side-by-side comparison of results from full liberalization of agricultural and non-agricultural support.
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Duclos, Diane, Bob Okello, Godefroid Muzalia, and Melissa Parker. Key considerations: Home-based care for mpox in Central and East Africa. Institute of Development Studies, 2025. https://doi.org/10.19088/sshap.2025.026.
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In September 2023, an outbreak of mpox caused by the monkeypox virus (MPXV) clade Ib was reported in Kamituga, a mining region in the Eastern Democratic Republic of the Congo (DRC). More cases of mpox started to be reported across the country and in neighbouring countries in the east, including Rwanda, Uganda and Burundi.1 In February 2025, the Africa Centres for Disease Control and Prevention and the Director-General of the World Health Organization (WHO) determined that the ongoing upsurge of mpox continues to be a public health emergency of international concern (as first declared in August 2024). Home-based care (HBC) – care provided in the private home of a person – often takes place informally for a wide range of reasons during epidemics. Home-based models of care are increasingly being explored by Ministries of Health as a strategy for managing outbreaks and providing treatment for mild forms of diseases, particularly in resource-limited settings. Reasons to implement HBC for mild forms of diseases include to provide care when there is a lack of access to or overburdened services, to prevent a risk of infection in health facilities, to accommodate people’s preferences and to empower the public when HBC is implemented in partnership with community members. Home-based models of c are for mpox should not supplant investments in the health system, but should be designed as a component of primary healthcare. Past experiences with HBC during outbreaks such as HIV and COVID-19 offer valuable lessons. However, the unique transmission dynamics of mpox – especially the risks it poses to children and those who are immunologically vulnerable in the home – require careful consideration. To date, attention has focused on infection, prevention and control (IPC) and water, sanitation and hygiene (WASH) in the home. Other aspects of mpox management and care at home also need to be considered. It is particularly important to recognise that mpox is not only a biomedical event: it is also a social phenomenon, impacting livelihoods, relationships, well-being and access to care and protection. Also, a lack of income in the absence of financial support is likely to hinder peoples’ ability to follow isolation guidance. This brief outlines key considerations on health system requirements for safe and inclusive HBC. It also foregrounds structural constraints and socio-political dynamics shaping understandings and practices of HBC, taking into consideration local and gendered perspectives on home and caregiving. The brief also examines how ongoing funding cuts in global health, humanitarian aid and development assistance are straining the capacity of both community-based initiatives and healthcare systems, further complicating home and community-based response efforts. The focus is on Central and East Africa in particular. The brief draws on conversations with experts and health actors active or knowledgeable in the region and outbreak, or both; the authors’ own expertise; and academic and grey literature on HBC and histories of epidemics in Central and East Africa. The brief includes two cases studies based on recent research in Uganda and the DRC.
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Otioma, Chuks, and Iain MacNeil. Robo Advisors as a Use Case of AI Systems: Linking Responsible Business Practices, Compliance and Outcomes. University of Glasgow and University of Strathclyde, 2025. https://doi.org/10.36399/gla.pubs.351605.
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In this paper, we explore the workings of robo-advisors as an example of AI-based systems. We discuss the performance and challenges of robo-advice, as well as offer reflections on how and why robo-advice as part of the broader fintech and financial services sector intersects practices in AI systems, regulation and compliance. We draw attention to the implications for explainable AI, the role of humans in the loop, compliance and business practices. Our approach focuses on how the AI capabilities in robo-advisors can help to build responsible business practices and compliance elements into operating models and business processes. We explore how these interactions apply to selected use cases in the UK and discuss implications for improvements in responsible business practices, regulations and consumer/client outcomes.
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Briand, Etienne, Massimiliano Marcellino, and Dalibor Stevanovic. Inflation, Attention and Expectations. CIRANO, 2025. https://doi.org/10.54932/qxot2239.
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We investigate the role of attention in shaping inflation dynamics. To measure the general public attention, we utilize Google Trends (GT) data for keywords such as "inflation." For professional attention, we construct an indicator based on the standardized count of Wall Street Journal (WSJ) articles with "inflation" in their titles. Through empirical analysis, we show that attention significantly impacts inflation dynamics, even when accounting for traditional inflation-related factors. Macroeconomic theory suggests that expectations formation is a natural mechanism to explain these findings. We find support for this hypothesis by measuring a decrease in professional forecasters’ information rigidity during periods of high attention. In contrast to prior research, our findings highlight the critical roles of media communication and public attention in shaping aggregate inflation expectations. We then develop a theoretical model that captures our stylized facts, showing that both inflation dynamics and forecaster expectations are regime-dependent. Finally, we examine the implications of this framework for the effectiveness of monetary policy.
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.
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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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Rizzo, Tesalia. Shaping political trust through participatory governance in Lat in America. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003601.
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This paper critically assesses research that examines the link between participatory institutions and political trust in the context of developing countries, with a focus on Latin America. A significant limitation in the systematic accumulation of knowledge in this field is inattention to identifying a clear causal chain through which citizen participation shapes political, economic, and attitudinal outcomes such as political trust. This is particularly important in the Latin American case where constitutionally stated objectives of participatory governance include the improvement of citizen welfare as well as strengthening of political trust in public institutions. Future work should concentrate in providing clear and testable models of the complex relationship between participatory mechanisms, policy, governance, and trust, with particular attention to what mediates and moderates this relationship. Additionally, empirical work done of the Latin America case should move away from a predominantly case-study based and macro-level perspective in the study of participatory institutions to micro-level studies from the citizens point of view. A new frontier for the study of participatory governance in Latin America lies in understanding how citizens experiences with and expectations of participatory institutions as well as the policy outcomes delivered by these institutions shape political trust.