scholarly journals Continuous authentication using deep neural networks ensemble on keystroke dynamics

2021 ◽  
Vol 7 ◽  
pp. e525
Author(s):  
Lerina Aversano ◽  
Mario Luca Bernardi ◽  
Marta Cimitile ◽  
Riccardo Pecori
Keyword(s):  
Deep Neural Networks ◽  
Lower Cost ◽  
Keystroke Dynamics ◽  
Keystroke Analysis ◽  
Real World Datasets ◽  
Neural Network Classifiers ◽  
Definition Of ◽  
Proper Group ◽  
Neural Networks Ensemble ◽  
Allocation Strategies

During the last years, several studies have been proposed about user identification by means of keystroke analysis. Keystroke dynamics has a lower cost when compared to other biometric-based methods since such a system does not require any additional specific sensor, apart from a traditional keyboard, and it allows the continuous identification of the users in the background as well. The research proposed in this paper concerns (i) the creation of a large integrated dataset of users typing on a traditional keyboard obtained through the integration of three real-world datasets coming from existing studies and (ii) the definition of an ensemble learning approach, made up of basic deep neural network classifiers, with the objective of distinguishing the different users of the considered dataset by exploiting a proper group of features able to capture their typing style. After an optimization phase, in order to find the best possible base classifier, we evaluated the ensemble super-classifier comparing different voting techniques, namely majority and Bayesian, as well as training allocation strategies, i.e., random and K-means. The approach we propose has been assessed using the created very large integrated dataset and the obtained results are very promising, achieving an accuracy of up to 0.997 under certain evaluation conditions.

scholarly journals A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 576
Author(s):  
Maurizio Polano ◽  
Emanuele Fabbiani ◽  
Eva Adreuzzi ◽  
Federica Di Cintio ◽  
Luca Bedon ◽  
...  
Keyword(s):  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Low Grade ◽  
Machine Learning Classification ◽  
Immune State ◽  
Out Of Sample ◽  
Cancer Genome Atlas ◽  
The Central Nervous System ◽  
Neural Network Classifiers ◽  
Definition Of

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state.

scholarly journals Resposta de Handroanthus heptaphyllus Mattos em diferentes volumes de substrato e adubação de base.

FLORESTA ◽  
2020 ◽  
Vol 50 (4) ◽  
pp. 1770
Author(s):  
Thaise Da Silva TONETTO ◽  
Maristela Machado Araujo ◽  
Álvaro Luís Pasquetti Berghetti ◽  
Márcio Carlos Navroski
Keyword(s):  
Controlled Release ◽  
Quality Index ◽  
Lower Cost ◽  
Dry Mass ◽  
High Dose ◽  
Native Forest ◽  
Forest Species ◽  
Definition Of ◽  
Appropriate Practices ◽  
Dickson Quality Index

Handroanthus heptaphyllus (Martius) Mattos response in different volumes of substrate and base fertilization. The native forest species require the adoption of appropriate practices in the nursery, such as the definition of the container volume and base fertilizer, to develop seedlings at lower cost. Thus, this study aimed to verify the performance of Handroanthus heptaphyllus seedlings grown in nurseries in different sizes of containers and different dosage of controlled release fertilizer (CRF). There was interaction between the volume of container and the fertilization for the height (H), stem diameter (SD), and H/SD relation. The shoot dry mass variable (SDM), root dry mass (RDM), total dry mass (TDM), SDM/RDM relation, Dickson quality index (DCI) and leaf area (LA) were influenced only by the base fertilizer. The highest values for these variables were, respectively, 15.68 cm; 6.55 mm, 2.58 cm mm-1; 2.05 g; 2.03 g; 3.84 g; 1.46; 1.15 and 246.97 cm². The H. heptaphyllus species is nutritionally demanding, positively responding to the high dose of CRF. Seedlings of H. heptaphyllus produced in tubes of 180 cm³ with 7.0 g L-1 of CRF present adequate growth.

scholarly journals Label Distribution for Learning with Noisy Labels

2020 ◽  
Author(s):  
Yun-Peng Liu ◽  
Ning Xu ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Confidence Estimation ◽  
Novel Method ◽  
Real World Datasets ◽  
Label Distribution ◽  
Noisy Labels

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore some labels become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean labels and noisy labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.

Deep Neural Networks Ensemble for Lung Nodule Detection on Chest CT Scans

2021 ◽  
Author(s):  
Pasquale Ardimento ◽  
Lerina Aversano ◽  
Mario Luca Bernardi ◽  
Marta Cimitile
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Lung Nodule ◽  
Chest Ct ◽  
Ct Scans ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Neural Networks Ensemble

scholarly journals Learning Improved Semantic Representations with Tree-Structured LSTM for Hashtag Recommendation: An Experimental Study

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 127 ◽  
Author(s):  
Rui Zhu ◽  
Delu Yang ◽  
Yang Li
Keyword(s):  
Deep Neural Networks ◽  
Semantic Information ◽  
Syntactic Structure ◽  
Text Representation ◽  
Neural Models ◽  
Textual Features ◽  
Real World Datasets ◽  
Core Idea ◽  
Significant Attention ◽  
Better Than

A hashtag is a type of metadata tag used on social networks, such as Twitter and other microblogging services. Hashtags indicate the core idea of a microblog post and can help people to search for specific themes or content. However, not everyone tags their posts themselves. Therefore, the task of hashtag recommendation has received significant attention in recent years. To solve the task, a key problem is how to effectively represent the text of a microblog post in a way that its representation can be utilized for hashtag recommendation. We study two major kinds of text representation methods for hashtag recommendation, including shallow textual features and deep textual features learned by deep neural models. Most existing work tries to use deep neural networks to learn microblog post representation based on the semantic combination of words. In this paper, we propose to adopt Tree-LSTM to improve the representation by combining the syntactic structure and the semantic information of words. We conduct extensive experiments on two real world datasets. The experimental results show that deep neural models generally perform better than traditional methods. Specially, Tree-LSTM achieves significantly better results on hashtag recommendation than standard LSTM, with a 30% increase in F1-score, which indicates that it is promising to utilize syntactic structure in the task of hashtag recommendation.

scholarly journals Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations

2021 ◽  
Author(s):  
Yingheng Wang ◽  
Yaosen Min ◽  
Erzhuo Shao ◽  
Ji Wu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Structural Information ◽  
Molecular Graph ◽  
Representation Learning ◽  
Graph Representation ◽  
Input Graph ◽  
Recent Success ◽  
Real World Datasets ◽  
Comparative Results

ABSTRACTLearning generalizable, transferable, and robust representations for molecule data has always been a challenge. The recent success of contrastive learning (CL) for self-supervised graph representation learning provides a novel perspective to learn molecule representations. The most prevailing graph CL framework is to maximize the agreement of representations in different augmented graph views. However, existing graph CL frameworks usually adopt stochastic augmentations or schemes according to pre-defined rules on the input graph to obtain different graph views in various scales (e.g. node, edge, and subgraph), which may destroy topological semantemes and domain prior in molecule data, leading to suboptimal performance. Therefore, designing parameterized, learnable, and explainable augmentation is quite necessary for molecular graph contrastive learning. A well-designed parameterized augmentation scheme can preserve chemically meaningful structural information and intrinsically essential attributes for molecule graphs, which helps to learn representations that are insensitive to perturbation on unimportant atoms and bonds. In this paper, we propose a novel Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations, MolCLE for brevity, that self-adaptively incorporates chemically significative information from both topological and semantic aspects of molecular graphs. Specifically, we apply deep neural networks to parameterize the augmentation process for both the molecular graph topology and atom attributes, to highlight contributive molecular substructures and recognize underlying chemical semantemes. Comprehensive experiments on a variety of real-world datasets demonstrate that our proposed method consistently outperforms compared baselines, which verifies the effectiveness of the proposed framework. Detailedly, our self-supervised MolCLE model surpasses many supervised counterparts, and meanwhile only uses hundreds of thousands of parameters to achieve comparative results against the state-of-the-art baseline, which has tens of millions of parameters. We also provide detailed case studies to validate the explainability of augmented graph views.CCS CONCEPTS• Mathematics of computing → Graph algorithms; • Applied computing → Bioinformatics; • Computing methodologies → Neural networks; Unsupervised learning.

Real-Time Emotional Recognition for Sociable Robotics Based on Deep Neural Networks Ensemble

2019 ◽  
pp. 171-180 ◽  
Author(s):  
Nadir Kamel Benamara ◽  
Mikel Val-Calvo ◽  
José Ramón Álvarez-Sánchez ◽  
Alejandro Díaz-Morcillo ◽  
José Manuel Ferrández Vicente ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Deep Neural Networks ◽  
Emotional Recognition ◽  
Neural Networks Ensemble

scholarly journals Deep neural networks ensemble for detecting medication mentions in tweets

2019 ◽  
Vol 26 (12) ◽  
pp. 1618-1626 ◽  
Author(s):  
Davy Weissenbacher ◽  
Abeed Sarker ◽  
Ari Klein ◽  
Karen O’Connor ◽  
Arjun Magge ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Final Decision ◽  
Drug Products ◽  
Learning Classifier ◽  
Twitter Data ◽  
Spelling Variant ◽  
Twitter Users ◽  
Trained Neural Network ◽  
Neural Networks Ensemble

Abstract Objective Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step toward incorporating Twitter data in pharmacoepidemiologic research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names suffer from low recall due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Materials and Methods We present Kusuri, an Ensemble Learning classifier able to identify tweets mentioning drug products and dietary supplements. Kusuri (薬, “medication” in Japanese) is composed of 2 modules: first, 4 different classifiers (lexicon based, spelling variant based, pattern based, and a weakly trained neural network) are applied in parallel to discover tweets potentially containing medication names; second, an ensemble of deep neural networks encoding morphological, semantic, and long-range dependencies of important words in the tweets makes the final decision. Results On a class-balanced (50-50) corpus of 15 005 tweets, Kusuri demonstrated performances close to human annotators with an F1 score of 93.7%, the best score achieved thus far on this corpus. On a corpus made of all tweets posted by 112 Twitter users (98 959 tweets, with only 0.26% mentioning medications), Kusuri obtained an F1 score of 78.8%. To the best of our knowledge, Kusuri is the first system to achieve this score on such an extremely imbalanced dataset. Conclusions The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness, and is ready to be integrated in pharmacovigilance, toxicovigilance, or more generally, public health pipelines that depend on medication name mentions.

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