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Journal articles on the topic 'Metric learning paradigm'

Author: Grafiati

Published: 6 September 2023

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1

Brockmeier, Austin J., John S. Choi, Evan G. Kriminger, Joseph T. Francis, and Jose C. Principe. "Neural Decoding with Kernel-Based Metric Learning." Neural Computation 26, no. 6 (2014): 1080–107. http://dx.doi.org/10.1162/neco_a_00591.

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In studies of the nervous system, the choice of metric for the neural responses is a pivotal assumption. For instance, a well-suited distance metric enables us to gauge the similarity of neural responses to various stimuli and assess the variability of responses to a repeated stimulus—exploratory steps in understanding how the stimuli are encoded neurally. Here we introduce an approach where the metric is tuned for a particular neural decoding task. Neural spike train metrics have been used to quantify the information content carried by the timing of action potentials. While a number of metric

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Saha, Soumadeep, Utpal Garain, Arijit Ukil, Arpan Pal, and Sundeep Khandelwal. "MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems." PLOS ONE 18, no. 8 (2023): e0283895. http://dx.doi.org/10.1371/journal.pone.0283895.

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When judging the quality of a computational system for a pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in the multi-label paradigm, they are being widely adopted to diagnostics and digital therapeutics. Metrics are usually borrowed from machine learning literature, and the current consensus is to report results on a diverse set of metrics. It is infeasible to compare efficacy of computational systems which have been evaluated on different sets of metrics. From a diagnostic

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Gong, Xiuwen, Dong Yuan, and Wei Bao. "Online Metric Learning for Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4012–19. http://dx.doi.org/10.1609/aaai.v34i04.5818.

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Existing research into online multi-label classification, such as online sequential multi-label extreme learning machine (OSML-ELM) and stochastic gradient descent (SGD), has achieved promising performance. However, these works lack an analysis of loss function and do not consider label dependency. Accordingly, to fill the current research gap, we propose a novel online metric learning paradigm for multi-label classification. More specifically, we first project instances and labels into a lower dimension for comparison, then leverage the large margin principle to learn a metric with an efficie

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Qiu, Wei. "Based on Semi-Supervised Clustering with the Boost Similarity Metric Method for Face Retrieval." Applied Mechanics and Materials 543-547 (March 2014): 2720–23. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2720.

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The focus of this paper is on Metric Learning, with particular interest in incorporating side information to make it semi-supervised. This study is primarily motivated by an application: face-image clustering. In the paper introduces metric learning and semi-supervised clustering, Boost the similarity metric learning method that adapt the underlying similarity metric used by the clustering algorithm. we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called Boost the Similarity Metric Method for Face Retrieval, Experimental results demonst

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Xiao, Qiao, Khuan Lee, Siti Aisah Mokhtar, et al. "Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review." Applied Sciences 13, no. 8 (2023): 4964. http://dx.doi.org/10.3390/app13084964.

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Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A tota

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Niu, Gang, Bo Dai, Makoto Yamada, and Masashi Sugiyama. "Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization." Neural Computation 26, no. 8 (2014): 1717–62. http://dx.doi.org/10.1162/neco_a_00614.

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We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For metric learning, entropy regularization improves manifold regularization by considering the dissimilarity information of unlabeled data in the unsupervised part, and hence it allows the supervised and unsup

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Wilde, Henry, Vincent Knight, and Jonathan Gillard. "Evolutionary dataset optimisation: learning algorithm quality through evolution." Applied Intelligence 50, no. 4 (2019): 1172–91. http://dx.doi.org/10.1007/s10489-019-01592-4.

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AbstractIn this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark datasets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the ‘best performing’. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs wel

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Zhukov, Alexey, Jenny Benois-Pineau, and Romain Giot. "Evaluation of Explanation Methods of AI - CNNs in Image Classification Tasks with Reference-based and No-reference Metrics." Advances in Artificial Intelligence and Machine Learning 03, no. 01 (2023): 620–46. http://dx.doi.org/10.54364/aaiml.2023.1143.

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The most popular methods in AI-machine learning paradigm are mainly black boxes. This is why explanation of AI decisions is of emergency. Although dedicated explanation tools have been massively developed, the evaluation of their quality remains an open research question. In this paper, we generalize the methodologies of evaluation of post-hoc explainers of CNNs’ decisions in visual classification tasks with reference and no-reference based metrics. We apply them on our previously developed explainers (FEM1 , MLFEM), and popular Grad-CAM. The reference-based metrics are Pearson correlation coe

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Pinto, Danna, Anat Prior, and Elana Zion Golumbic. "Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning." Neurobiology of Language 3, no. 2 (2022): 214–34. http://dx.doi.org/10.1162/nol_a_00061.

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Abstract Statistical learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive electroencephalograph (EEG) recordings of neural activity in humans. Important

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Gomoluch, Paweł, Dalal Alrajeh, and Alessandra Russo. "Learning Classical Planning Strategies with Policy Gradient." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 637–45. http://dx.doi.org/10.1609/icaps.v29i1.3531.

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A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the approaches. This enables using policy gradient to learn search strategies tailored to a specific distributi

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Dou, Jason Xiaotian, Lei Luo, and Raymond Mingrui Yang. "An Optimal Transport Approach to Deep Metric Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12935–36. http://dx.doi.org/10.1609/aaai.v36i11.21604.

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Capturing visual similarity among images is the core of many computer vision and pattern recognition tasks. This problem can be formulated in such a paradigm called metric learning. Most research in the area has been mainly focusing on improving the loss functions and similarity measures. However, due to the ignoring of geometric structure, existing methods often lead to sub-optimal results. Thus, several recent research methods took advantage of Wasserstein distance between batches of samples to characterize the spacial geometry. Although these approaches can achieve enhanced performance, the

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Wang, Yabin, Zhiheng Ma, Zhiwu Huang, Yaowei Wang, Zhou Su, and Xiaopeng Hong. "Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10209–17. http://dx.doi.org/10.1609/aaai.v37i8.26216.

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This paper focuses on the prevalent stage interference and stage performance imbalance of incremental learning. To avoid obvious stage learning bottlenecks, we propose a new incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task at each stage, without interference from others. To be concrete, to aggregate multiple stage classifiers as a uniform one impartially, we first introduce a temperature-controlled energy metric for indicating the confidence score levels of the stage classifiers. We then propose an anchor-based energy self-norm

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Ge, Ce, Jingyu Wang, Qi Qi, Haifeng Sun, Tong Xu, and Jianxin Liao. "Semi-transductive Learning for Generalized Zero-Shot Sketch-Based Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7678–86. http://dx.doi.org/10.1609/aaai.v37i6.25931.

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Sketch-based image retrieval (SBIR) is an attractive research area where freehand sketches are used as queries to retrieve relevant images. Existing solutions have advanced the task to the challenging zero-shot setting (ZS-SBIR), where the trained models are tested on new classes without seen data. However, they are prone to overfitting under a realistic scenario when the test data includes both seen and unseen classes. In this paper, we study generalized ZS-SBIR (GZS-SBIR) and propose a novel semi-transductive learning paradigm. Transductive learning is performed on the image modality to expl

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De Santis, Enrico, Alessio Martino, and Antonello Rizzi. "On component-wise dissimilarity measures and metric properties in pattern recognition." PeerJ Computer Science 8 (October 10, 2022): e1106. http://dx.doi.org/10.7717/peerj-cs.1106.

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In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in w

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Jaiswal, Mimansa, and Emily Mower Provost. "Privacy Enhanced Multimodal Neural Representations for Emotion Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7985–93. http://dx.doi.org/10.1609/aaai.v34i05.6307.

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Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Yet, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage diffe

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Yuan, Fei, Longtu Zhang, Huang Bojun, and Yaobo Liang. "Simpson's Bias in NLP Training." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14276–83. http://dx.doi.org/10.1609/aaai.v35i16.17679.

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In most machine learning tasks, we evaluate a model M on a given data population S by measuring a population-level metric F(S;M). Examples of such evaluation metric F include precision/recall for (binary) recognition, the F1 score for multi-class classification, and the BLEU metric for language generation. On the other hand, the model M is trained by optimizing a sample-level loss G(S_t; M) at each learning step t, where S_t is a subset of S (a.k.a. the mini-batch). Popular choices of G include cross-entropy loss, the Dice loss, and sentence-level BLEU scores. A fundamental assumption behind t

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Khan, Koffka, and Wayne Goodridge. "Comparative study of One-Shot Learning in Dynamic Adaptive Streaming over HTTP : A Taxonomy-Based Analysis." International Journal of Advanced Networking and Applications 15, no. 01 (2023): 5822–30. http://dx.doi.org/10.35444/ijana.2023.15112.

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Dynamic Adaptive Streaming over HTTP (DASH) has revolutionized multimedia content delivery, enabling efficient video streaming over the internet. One-shot learning, a machine learning paradigm that allows recognition of new classes or objects with minimal training examples, holds promise for enhancing DASH systems. In this comparative study, we present a taxonomy-based analysis of one-shot learning techniques in the context of DASH, examining four taxonomies to provide a comprehensive understanding of their applications, evaluation metrics, and datasets. The first taxonomy focuses on categoriz

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WOLFMAN, STEVEN A., and DANIEL S. WELD. "Combining linear programming and satisfiability solving for resource planning." Knowledge Engineering Review 16, no. 1 (2001): 85–99. http://dx.doi.org/10.1017/s0269888901000017.

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Compilation to Boolean satisfiability has become a powerful paradigm for solving artificial intelligence problems. However, domains that require metric reasoning cannot be compiled efficiently to satisfiability even if they would otherwise benefit from compilation. We address this problem by combining techniques from the artificial intelligence and operations research communities. In particular, we introduce the LCNF (Linear Conjunctive Normal Form) representation that combines propositional logic with metric constraints. We present LPSAT (Linear Programming plus SATisfiability), an engine tha

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Lin, Jianman, Jiantao Lin, Yuefang Gao, Zhijing Yang, and Tianshui Chen. "Webly Supervised Fine-Grained Image Recognition with Graph Representation and Metric Learning." Electronics 11, no. 24 (2022): 4127. http://dx.doi.org/10.3390/electronics11244127.

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The aim of webly supervised fine-grained image recognition (FGIR) is to distinguish sub-ordinate categories based on data retrieved from the Internet, which can significantly mitigate the dependence of deep learning on manually annotated labels. Most current fine-grained image recognition algorithms use a large-scale data-driven deep learning paradigm, which relies heavily on manually annotated labels. However, there is a large amount of weakly labeled free data on the Internet. To utilize fine-grained web data effectively, this paper proposes a Graph Representation and Metric Learning (GRML)

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Samann, Fady Esmat Fathel, Adnan Mohsin Abdulazeez, and Shavan Askar. "Fog Computing Based on Machine Learning: A Review." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 12 (2021): 21. http://dx.doi.org/10.3991/ijim.v15i12.21313.

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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) at

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Elfakharany, Ahmed, and Zool Hilmi Ismail. "End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System." Applied Sciences 11, no. 7 (2021): 2895. http://dx.doi.org/10.3390/app11072895.

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In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot’s steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index (TAI), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centrali

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Sotiropoulos, Dionisios N., Efthimios Alepis, Katerina Kabassi, Maria K. Virvou, George A. Tsihrintzis, and Evangelos Sakkopoulos. "Artificial Immune System-Based Learning Style Stereotypes." International Journal on Artificial Intelligence Tools 28, no. 04 (2019): 1940008. http://dx.doi.org/10.1142/s0218213019400086.

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This paper addresses the problem of extracting fundamental learning style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems (AIS). We present an unsupervised computational mechanism which exhibits the ability to reveal the inherent group structure of learning patterns that pervade a given set of educational profiles. We rely on the construction of an Artificial Immune Network (AIN) of learning style exemplars by proposing a correlation-based distance metric. This choice is actually imposed by the categoric nature of the

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Mwata-Velu, Tat’y, Juan Gabriel Avina-Cervantes, Jose Ruiz-Pinales, et al. "Improving Motor Imagery EEG Classification Based on Channel Selection Using a Deep Learning Architecture." Mathematics 10, no. 13 (2022): 2302. http://dx.doi.org/10.3390/math10132302.

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Recently, motor imagery EEG signals have been widely applied in Brain–Computer Interfaces (BCI). These signals are typically observed in the first motor cortex of the brain, resulting from the imagination of body limb movements. For non-invasive BCI systems, it is not apparent how to locate the electrodes, optimizing the accuracy for a given task. This study proposes a comparative analysis of channel signals exploiting the Deep Learning (DL) technique and a public dataset to locate the most discriminant channels. EEG channels are usually selected based on the function and nomenclature of elect

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Liu, Pingping, Guixia Gou, Xue Shan, Dan Tao, and Qiuzhan Zhou. "Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval." Sensors 20, no. 1 (2020): 291. http://dx.doi.org/10.3390/s20010291.

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A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet

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Li, Hui, Jinhao Liu, and Dian Wang. "A Fast Instance Segmentation Technique for Log End Faces Based on Metric Learning." Forests 14, no. 4 (2023): 795. http://dx.doi.org/10.3390/f14040795.

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The diameter of the logs on a vehicle is a critical part of the logistics and transportation of logs. However, the manual size-checking method is inefficient and affects the efficiency of log transportation. The example segmentation methods can generate masks for each log end face, which helps automate the check gauge of logs and improve efficiency. The example segmentation model uses rectangle detection to identify each end face and then traverses the rectangular boxes for mask extraction. The traversal of rectangular boxes increases the time consumption of the model and lacks separate handli

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Motaung, William B., Kingsley A. Ogudo, and Chabalala S. Chabalala. "Optimal Video Compression Parameter Tuning for Digital Video Broadcasting (DVB) using Deep Reinforcement Learning." International Conference on Intelligent and Innovative Computing Applications 2022 (December 31, 2022): 270–76. http://dx.doi.org/10.59200/iconic.2022.030.

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DVB (digital video broadcasting) has undergone an enormous paradigm shift, especially through internet streaming that utilizes multiple channels (i.e., secured hypertext transfer protocols). However, due to the limitations of the current communication network infrastructure, video signals need to be compressed before transmission. Whereas most recent research has concentrated and focused on assessing video quality, little to no study has worked on improving the compression processes of digital video signals in lightweight DVB setups. This study provides a video compression strategy (DRL-VC) th

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Benvenuto, Giovana A., Marilaine Colnago, Maurício A. Dias, Rogério G. Negri, Erivaldo A. Silva, and Wallace Casaca. "A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration." Bioengineering 9, no. 8 (2022): 369. http://dx.doi.org/10.3390/bioengineering9080369.

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In ophthalmology, the registration problem consists of finding a geometric transformation that aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient. Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model. Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by mos

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Sakakushev, Boris E., Blagoi I. Marinov, Penka P. Stefanova, Stefan St Kostianev, and Evangelos K. Georgiou. "Striving for Better Medical Education: the Simulation Approach." Folia Medica 59, no. 2 (2017): 123–31. http://dx.doi.org/10.1515/folmed-2017-0039.

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AbstractMedical simulation is a rapidly expanding area within medical education due to advances in technology, significant reduction in training hours and increased procedural complexity. Simulation training aims to enhance patient safety through improved technical competency and eliminating human factors in a risk free environment. It is particularly applicable to a practical, procedure-orientated specialties.Simulation can be useful for novice trainees, experienced clinicians (e.g. for revalidation) and team building. It has become a cornerstone in the delivery of medical education, being a

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Kuang, Jiachen, Tangfei Tao, Qingqiang Wu, et al. "Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions." Sensors 22, no. 17 (2022): 6535. http://dx.doi.org/10.3390/s22176535.

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In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data. As a result, there is an increasing demand for both researchers and engineers for fault identification with scarce data. To address this issue, an innovative domain-adaptive prototype-recalibrated network (DAPRN) based on a transductive learning paradigm and prototype recalibration strategy (PRS) is proposed, which has the potential to promote th

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Manzoor, Sumaira, Ye-Chan An, Gun-Gyo In, Yueyuan Zhang, Sangmin Kim, and Tae-Yong Kuc. "SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model." Sensors 23, no. 10 (2023): 4906. http://dx.doi.org/10.3390/s23104906.

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Pedestrian tracking is a challenging task in the area of visual object tracking research and it is a vital component of various vision-based applications such as surveillance systems, human-following robots, and autonomous vehicles. In this paper, we proposed a single pedestrian tracking (SPT) framework for identifying each instance of a person across all video frames through a tracking-by-detection paradigm that combines deep learning and metric learning-based approaches. The SPT framework comprises three main modules: detection, re-identification, and tracking. Our contribution is a signific

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Xu, Yanbing, Yanmei Zhang, Tingxuan Yue, Chengcheng Yu, and Huan Li. "Graph-Based Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification." Remote Sensing 15, no. 4 (2023): 1125. http://dx.doi.org/10.3390/rs15041125.

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Due to a lack of labeled samples, deep learning methods generally tend to have poor classification performance in practical applications. Few-shot learning (FSL), as an emerging learning paradigm, has been widely utilized in hyperspectral image (HSI) classification with limited labeled samples. However, the existing FSL methods generally ignore the domain shift problem in cross-domain scenes and rarely explore the associations between samples in the source and target domain. To tackle the above issues, a graph-based domain adaptation FSL (GDAFSL) method is proposed for HSI classification with

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Alshammari, Abdulaziz, and Rakan C. Chabaan. "Sppn-Rn101: Spatial Pyramid Pooling Network with Resnet101-Based Foreign Object Debris Detection in Airports." Mathematics 11, no. 4 (2023): 841. http://dx.doi.org/10.3390/math11040841.

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Over the past few years, aviation security has turned into a vital domain as foreign object debris (FOD) on the airport paved path possesses an enormous possible threat to airplanes at the time of takeoff and landing. Hence, FOD’s precise identification remains significant for assuring airplane flight security. The material features of FOD remain the very critical criteria for comprehending the destruction rate endured by an airplane. Nevertheless, the most frequent identification systems miss an efficient methodology for automated material identification. This study proffers a new FOD techniq

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Li, Shuyuan, Huabin Liu, Rui Qian, et al. "TA2N: Two-Stage Action Alignment Network for Few-Shot Action Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1404–11. http://dx.doi.org/10.1609/aaai.v36i2.20029.

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Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradigm, which learns to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not ideal since different action instances may show distinctive temporal distribution, resulting in severe misalignment issues across query and support videos. In this paper, we arrest this problem from two distinct aspects -- action duration misalignment and action evolution misalignmen

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Alonso-Betanzos, Amparo, Verónica Bolón-Canedo, Guy R. Heyndrickx, and Peter L. M. Kerkhof. "Exploring Guidelines for Classification of Major Heart Failure Subtypes by Using Machine Learning." Clinical Medicine Insights: Cardiology 9s1 (January 2015): CMC.S18746. http://dx.doi.org/10.4137/cmc.s18746.

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Background Heart failure (HF) manifests as at least two subtypes. The current paradigm distinguishes the two by using both the metric ejection fraction (EF) and a constraint for end-diastolic volume. About half of all HF patients exhibit preserved EF. In contrast, the classical type of HF shows a reduced EF. Common practice sets the cut-off point often at or near EF = 50%, thus defining a linear divider. However, a rationale for this safe choice is lacking, while the assumption regarding applicability of strict linearity has not been justified. Additionally, some studies opt for eliminating pa

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Uzair, Muhammad, Mohsen Eskandari, Li Li, and Jianguo Zhu. "Machine Learning Based Protection Scheme for Low Voltage AC Microgrids." Energies 15, no. 24 (2022): 9397. http://dx.doi.org/10.3390/en15249397.

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The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional meth

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Martinelli, M., C. J. A. P. Martins, S. Nesseris, et al. "Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes." Astronomy & Astrophysics 644 (December 2020): A80. http://dx.doi.org/10.1051/0004-6361/202039078.

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Context. In metric theories of gravity with photon number conservation, the luminosity and angular diameter distances are related via the Etherington relation, also known as the distance duality relation (DDR). A violation of this relation would rule out the standard cosmological paradigm and point to the presence of new physics. Aims. We quantify the ability of Euclid, in combination with contemporary surveys, to improve the current constraints on deviations from the DDR in the redshift range 0 < z < 1.6. Methods. We start with an analysis of the latest available data, improving previou

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Lyu, Yangxintong, Ionut Schiopu, Bruno Cornelis, and Adrian Munteanu. "Framework for Vehicle Make and Model Recognition—A New Large-Scale Dataset and an Efficient Two-Branch–Two-Stage Deep Learning Architecture." Sensors 22, no. 21 (2022): 8439. http://dx.doi.org/10.3390/s22218439.

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In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation Systems (ITS). Accurate and efficient VMMR systems are required in real-world applications including intelligent surveillance and autonomous driving. The paper introduces a new large-scale dataset and a novel deep learning paradigm for VMMR. A new large-scale dataset dubbed Diverse large-scale VMM (DVMM) is proposed collecting image-samples with the most popular vehicle brands operating in Europe. A novel VMMR framework is proposed which follows a

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Amari, Shun-ichi, Hyeyoung Park, and Tomoko Ozeki. "Singularities Affect Dynamics of Learning in Neuromanifolds." Neural Computation 18, no. 5 (2006): 1007–65. http://dx.doi.org/10.1162/neco.2006.18.5.1007.

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The parameter spaces of hierarchical systems such as multilayer perceptrons include singularities due to the symmetry and degeneration of hidden units. A parameter space forms a geometrical manifold, called the neuromanifold in the case of neural networks. Such a model is identified with a statistical model, and a Riemannian metric is given by the Fisher information matrix. However, the matrix degenerates at singularities. Such a singular structure is ubiquitous not only in multilayer perceptrons but also in the gaussian mixture probability densities, ARMA time-series model, and many other cas

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Voulodimos, Athanasios, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, and Nikolaos Doulamis. "A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images." Sensors 21, no. 6 (2021): 2215. http://dx.doi.org/10.3390/s21062215.

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Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these

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Anand, S. S., P. W. Hamilton, J. G. Hughes, and D. A. Bell. "On Prognostic Models, Artificial Intelligence and Censored Observations." Methods of Information in Medicine 40, no. 01 (2001): 18–24. http://dx.doi.org/10.1055/s-0038-1634459.

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AbstractThe development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On

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YAN, YUHONG, and HAN LIANG. "LAZY LEARNER ON DECISION TREE FOR RANKING." International Journal on Artificial Intelligence Tools 17, no. 01 (2008): 139–58. http://dx.doi.org/10.1142/s0218213008003819.

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This paper aims to improve probability-based ranking (e.g. AUC) under decision-tree paradigm. We observe the fact that probability-based ranking is to sort samples in terms of their class probabilities. Therefore, ranking is a relative evaluation metric among those samples. This motivates us to use a lazy learner to explicitly yield a set of unique class probabilities for a testing sample based on its similarities to the training samples within its neighborhood. We embed lazy learners at the leaves of a decision tree to give class probability assignments. This results in the first model, named

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Rayala, Venkat, and Satyanarayan Reddy Kalli. "Big Data Clustering Using Improvised Fuzzy C-Means Clustering." Revue d'Intelligence Artificielle 34, no. 6 (2020): 701–8. http://dx.doi.org/10.18280/ria.340604.

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Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of it is to categorize the data into clusters where objects are grouped into the particular category. However, there are various challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises th

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Longo, Mathias, Matías Hirsch, Cristian Mateos, and Alejandro Zunino. "Towards Integrating Mobile Devices into Dew Computing: A Model for Hour-Wise Prediction of Energy Availability." Information 10, no. 3 (2019): 86. http://dx.doi.org/10.3390/info10030086.

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With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days, without being plugged to the electricity grid. Nonetheless, misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew

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Tamm, Markus-Oliver, Yar Muhammad, and Naveed Muhammad. "Classification of Vowels from Imagined Speech with Convolutional Neural Networks." Computers 9, no. 2 (2020): 46. http://dx.doi.org/10.3390/computers9020046.

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Imagined speech is a relatively new electroencephalography (EEG) neuro-paradigm, which has seen little use in Brain-Computer Interface (BCI) applications. Imagined speech can be used to allow physically impaired patients to communicate and to use smart devices by imagining desired commands and then detecting and executing those commands in a smart device. The goal of this research is to verify previous classification attempts made and then design a new, more efficient neural network that is noticeably less complex (fewer number of layers) that still achieves a comparable classification accurac

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Dave, Chitrak Vimalbhai. "An Efficient Framework for Cost and Effort Estimation of Scrum Projects." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1478–87. http://dx.doi.org/10.22214/ijraset.2021.39030.

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Abstract: Software Process Models from its inception instill standardization and creates a generic culture of developing software for various IT industries. A great paradigm shift has been observed in terms of embracing Agile Development methodology as a viable development methodology in cross key business units. There is a buffet of agile methodologies comes under the umbrella of ASD, out of which Scrum got the highest popularity and acceptability index. Agile based software development is the need of immediate environment. There is an increasing demand for significant changes to software sys

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Galindo-Noreña, Steven, David Cárdenas-Peña, and Álvaro Orozco-Gutierrez. "Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks." Applied Sciences 10, no. 23 (2020): 8628. http://dx.doi.org/10.3390/app10238628.

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Brain–computer interface (BCI) systems communicate the human brain and computers by converting electrical activity into commands to use external devices. Such kind of system has become an alternative for interaction with the environment for people suffering from motor disabilities through the motor imagery (MI) paradigm. Despite being the most widespread, electroencephalography (EEG)-based MI systems are highly sensitive to noise and artifacts. Further, spatially close brain activity sources and variability among subjects hampers the system performance. This work proposes a methodology for the

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Feng, Jialiang, and Jie Gong. "AoI-Aware Optimization of Service Caching-Assisted Offloading and Resource Allocation in Edge Cellular Networks." Sensors 23, no. 6 (2023): 3306. http://dx.doi.org/10.3390/s23063306.

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The rapid development of the Internet of Things (IoT) has led to computational offloading at the edge; this is a promising paradigm for achieving intelligence everywhere. As offloading can lead to more traffic in cellular networks, cache technology is used to alleviate the channel burden. For example, a deep neural network (DNN)-based inference task requires a computation service that involves running libraries and parameters. Thus, caching the service package is necessary for repeatedly running DNN-based inference tasks. On the other hand, as the DNN parameters are usually trained in distribu

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Powers, David. "Unsupervised Learning of Linguistic Structure." International Journal of Corpus Linguistics 2, no. 1 (1997): 91–131. http://dx.doi.org/10.1075/ijcl.2.1.06pow.

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Computational Linguistics and Natural Language have long been targets for Machine Learning, and a variety of learning paradigms and techniques have been employed with varying degrees of success. In this paper, we review approaches which have adopted an unsupervised learning paradigm, explore the assumptions which underlie the techniques used, and develop an approach to empirical evaluation. We concentrate on a statistical framework based on N-grams, although we seek to maintain neurolinguistic plausibility. The model we adopt places putative linguistic units in focus and associates them with a

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Azam, Abu Bakr, Yu Qing Chang, Matthew Leong Tze Ker, et al. "818 Using deep learning approaches with mIF images to enhance T cell identification for tumor -automation of infiltrating lymphocytes (TILs) scoring on H&E images." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (2021): A855—A856. http://dx.doi.org/10.1136/jitc-2021-sitc2021.818.

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BackgroundExamining Hematoxylin & Eosin (H&E) images using brightfield microscopes is the gold standard of pathological diagnosis as it is an inexpensive method and provides basic information of tumors and other nuclei. Complementary to H&E-stained images, Immunohistochemical (IHC) images are crucial in identifying tumor subtypes and efficacy of treatment response. Other newer technologies such as Multiplex Immunofluorescence (mIF) in particular, identifies cells such as tumor infiltrating lymphocytes (TILs) which can be augmented via immunotherapy, an evolving form of cancer treat

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Samtani, Sagar, Yidong Chai, and Hsinchun Chen. "Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model." MIS Quarterly 46, no. 2 (2022): 911–46. http://dx.doi.org/10.25300/misq/2022/15392.

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Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- ba

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