Reviewerly/exHarmony huggingface dataset:
Age Estimation System Using Deep Residual Network Classification Method

The human face has biometric properties that are important for providing age information because of the aging process of the face. Automatic Age estimation is a difficult problem because the relationship between facial images and age is not very linear. Deep residual network (Resnet) is a neural network convolution architecture that was easier to optimize and can gain accuracy results from a considerably increasing depth. In this paper, we propose a new approach age estimation on convolution neural network (CNN) using the deep residual network (Resnet) model. Through the literature, Resnet achieves superior results when compared with other state-of-the-art image classifications. We compare a new generation of deep residual network called ResNeXt with Resnet and a basic linier regression model architecture.We user UTKFace dataset to evaluate the performance of residual network for age estimation of the range 1-100 years old. The result shows that the ResNeXt-50 (32×4d) architecture achieves a better age estimation results than Resnet-50 and linier regression.
Bootstrapping and multiple imputation ensemble approaches for classification problems

Presence of missing values in a dataset can adversely affect the performance of a classifier. Single and Multiple Imputation are normally performed to fill in the missing values. In this paper, we present several variants of combining single and multiple imputation with bootstrapping to create ens embles that can model uncertainty and diversity in the data, and that are robust to high missingness in the data. We present three ensemble strategies: bootstrapping on incomplete data followed by (i) single imputation and (ii) multiple imputation, and (iii) multiple imputation ensemble without bootstrapping. We perform an extensive evaluation of the performance of the these ensemble strategies on eight datasets by varying the missingness ratio. Our results show that bootstrapping followed by multiple imputation using expectation maximization is the most robust method even at high missingness ratio (up to 30%). For small missingness ratio (up to 10%) most of the ensemble methods perform equivalently but better than single imputation. Kappa-error plots suggest that accurate classifiers with reasonable diversity is the reason for this behaviour. A consistent observation in all the datasets suggests that for small missingness (up to 10%), bootstrapping on incomplete data without any imputation produces equivalent results to other ensemble methods.
MEMOn: Modular Environmental Monitoring Ontology to link heterogeneous Earth observed data

Earth observation (EO) systems play a significant role in environmental monitoring and the prediction of natural disasters. These systems generate a massive amount of heterogeneous data stored in different formats. The exploitation of this data is still limited while, in most cases, data are not linked, and sources are not interoperable. Hence, data cannot be exploited as an interoperable global knowledge graph to have more in-depth analyzes of environmental phenomena. Ontology, as a knowledge representation formalism, is a promising solution for the semantic interoperability between this data. In this work, we present a modular ontology for environmental monitoring developed based on an original agile methodology. The so-called MEMOn (Modular Environmental Monitoring Ontology) aims to support semantic interoperability, data integration, and linking of heterogeneous data collected through a variety of observation techniques and systems. We also present real use case studies to show the usefulness of the proposed ontology.
Topical keyphrase extraction with hierarchical semantic networks

Topical keyphrase extraction is used to summarize large collections of text documents. However, traditional methods cannot properly reflect the intrinsic semantics and relationships of keyphrases because they rely on a simple term-frequency-based process. Consequently, these methods are not effective in obtaining significant contextual knowledge. To resolve this, we propose a topical keyphrase extraction method based on a hierarchical semantic network and multiple centrality network measures that together reflect the hierarchical semantics of keyphrases. We conduct experiments on real data to examine the practicality of the proposed method and to compare its performance with that of existing topical keyphrase extraction methods. The results confirm that the proposed method outperforms state-of-the-art topical keyphrase extraction methods in terms of the representativeness of the selected keyphrases for each topic. The proposed method can effectively reflect intrinsic keyphrase semantics and interrelationships.
A Comprehensive Exploration on Spider with Fuzzy Decision Text-to-SQL Model

The challenge of natural language processing is from natural language to logical form (SQL). In this article, we present an fuzzy semantic to structured query language (F-SemtoSql) neural approach that is a fuzzy decision semantic deep network query model based on demand aggregation. It aims to address the problem of the complex and cross-domain text-to-SQL generation task. The corpus is trained as the input word vector of the model with LSTM and Word2Vec embedding technology. Combined with the dependency graph method, the problem of SQL statement generation is converted to slot filling. Complex tasks are divided into four levels via F-SemtoSql and constructed by the need of aggregation. At the same time, to avoid the order problem in the traditional model effectively, we have adopted the attention mechanism and used a fuzzy decision mechanism to improve the model decision. On the challenging text-to-SQL benchmark Spider and the other three datasets, F-SemtoSql achieves faster convergence and occupies the first position.
DVC analysis of a polymer material subjected to tensile loading with synchrotron radiation tomography

Subsurface deformation behavior of a polymeric material is studied through the digital volume correlation (DVC) technique. Fundamental principles of the DVC technique are presented and the supplemental state-of-the-art algorithmic schemes to improve the efficiency and accuracy of the DVC analysis are also introduced. Tensile tests on an epoxy material are performed in conjunction with synchrotron radiation tomography. In order to create randomly distributed grayscale values in the tomograms for the following image analysis, microscale high-density particles are embedded when the epoxy specimens are fabricated. 3D tomographic images taken at multiple loading steps are utilized for the DVC analysis. The performance of the present DVC analysis is evaluated with the experimental data.
Generating non-classical correlations between two superconductor qubits confined in a transmission cavity in dispersive limit under intrinsic noise

An analytical solution of the dynamical evolution of two qubits confined in a transmission line cavity is obtained in dispersive limit under intrinsic noise. The dynamics of non-classical correlations are investigated by using different measures based on the skew information quantifiers [including local quantum uncertainty and uncertainty-induced non-locality], the Bell function and logarithmic negativity for experimentally chosen parameters. It is found that the generation of the non-classical correlations and its robustness depend crucially on the transverse exchange interaction, the dissipation rate, the mean photon number, and the frequency shift. The phenomena of sudden disappearance and sudden appearance of entanglement are observed. The stationary correlations appear due to the intrinsic decoherence, which depends on the parameters of the chosen initial states.
Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT

Abstract IoT devices have limited memory, storage, and bandwidth, which makes it difficult for them to handle large and complex data. Therefore much of this data must be outsourced to the cloud. This outsourcing, however, creates privacy problems and allows for unauthorized access to the data. Ciphertext-policy attribute-based encryption (CP-ABE) schemes are the best solution for providing privacy and access control to the data in the cloud. However, CP-ABE schemes are not suitable for lightweight IoT devices because they create more computation overhead while performing encryption and decryption operations. In this chapter, we propose a lightweight CP-ABE scheme with a Walsh-Hadamard transform access structure for providing data privacy with access control in cloud-assisted IoT.
A Global-Best Guided Human Mental Search Algorithm with Random Clustering Strategy

Human mental search (HMS) is a recent population-based metaheuristic inspired by the exploration manner in the bid space of online auctions. It has three main operators: (1) mental search which explores the vicinity of each candidate solution based on Levy flight, (2) grouping which is performed using a clustering algorithm to find a promising area, and (3) moving towards the promising area. HMS has shown competitive performance in solving various optimisation problems.In this paper, an improved HMS algorithm, Global-Best Human Mental Search with Random Clustering Strategy (GHMS-RCS) is proposed as a variant of HMS for global optimisation. GHMS-RCS benefits from the information of global best solutions to improve the exploitation of the HMS algorithm. Also, to reduce the time complexity and enhance exploration and exploitation, a new strategy named random clustering is introduced to improve the grouping operator in HMS. Experimental results show that GHMS-RCS outperforms standard HMS as well as other population-based algorithms including particle swarm optimisation (PSO), shuffled frog-leaping algorithm (SFLA), and biogeography-based optimisation (BBO).
Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter

Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastructure-based sensing systems. However, a widespread use of such systems is circumvented by their complexity and thus, high costs. Within this paper, a generic interface is proposed, which enables a huge variety of sensors to be connected. The sensors are only required to measure very few features of the objects, if multiple distributed sensors with different viewing directions are available. Furthermore, a Labeled Multi-Bernoulli (LMB) filter is presented, which can not only handle such measurements, but also infers missing object information about the objects’ extents. The approach is evaluated on simulations and demonstrated on a real-world infrastructure setup.
Multi-View 3D Face Reconstruction in the Wild Using Siamese Networks

In this work, we present a novel learning based approach to reconstruct 3D faces from a single or multiple images. Our method uses a simple yet powerful architecture based on siamese neural networks that helps to extract relevant features from each view while keeping the models small. Instead of minimizing multiple objectives, we propose to simultaneously learn the 3D shape and the individual camera poses by using a single term loss based on the reprojection error, which generalizes from one to multiple views. This allows to globally optimize the whole scene without having to tune any hyperparameters and to achieve low reprojection errors, which are important for further texture generation. Finally, we train our model on a large scale dataset with more than 6,000 facial scans. We report competitive results in 3DFAW 2019 challenge, showing the effectiveness of our method.
An Innovative BERT-Based Readability Model

Readability is referred to as the degree of difficulty to which an given text (article) can be understood by readers. When readers are reading a text with high readability, they will achieve better comprehension and learning retention. However, it has been a long-standing critical challenge to develop effective readability prediction models that can automatically and accurately assess the readability of a given text. When building readability prediction models for the Chinese language, word segmentation ambiguity is often a knotty problem that will inevitably happen in the pre-processing of texts. In view of this, we present in this paper a novel readability prediction approach for the Chinese language, building on a recently proposed, so-called Bidirectional Encoder Representation from Transformers (BERT) model that can capture both syntactic and semantic information of a text directly from its character-level representation. With the BERT-based readability prediction model that takes consecutive character-level representations as its input, we effectively assess the readability of a given text without the need of performing error-prone word segmentation. We empirically evaluate the performance of our BERT-based readability prediction model on a benchmark task, by comparing it with a strong baseline that utilizes a celebrated classification model (named fastText) in conjunction with word-level presentations. The results demonstrate that the BERT-based model with character-level representations can perform on par with the fastText-based model with word-level representations, yielding the accuracy of 78.45% on average. This finding also offers the promise of conducting readability assessment of a text in Chinese directly based on character-level representations.
Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways

A signalling pathway is a sequence of chemical reactions initiated by a stimulus which in turn affects a receptor, and then through some intermediate steps cascades down to the final cell response. Based on the technique of flow cytometry, samples of cell-by-cell measurements are collected under each experimental condition, resulting in a collection of interventional data (assuming no latent variables are involved). Usually several external interventions are applied at different points of the pathway, the ultimate aim being the structural recovery of the underlying signalling network which we model as a causal Directed Acyclic Graph (DAG) using intervention calculus. The advantage of using interventional data, rather than purely observational one, is that identifiability of the true data generating DAG is enhanced. More technically a Markov equivalence class of DAGs, whose members are statistically indistinguishable based on observational data alone, can be further decomposed, using additional interventional data, into smaller distinct Interventional Markov equivalence classes. We present a Bayesian methodology for structural learning of Interventional Markov equivalence classes based on observational and interventional samples of multivariate Gaussian observations. Our approach is objective, meaning that it is based on default parameter priors requiring no personal elicitation; some flexibility is however allowed through a tuning parameter which regulates sparsity in the prior on model space. Based on an analytical expression for the marginal likelihood of a given Interventional Essential Graph, and a suitable MCMC scheme, our analysis produces an approximate posterior distribution on the space of Interventional Markov equivalence classes, which can be used to provide uncertainty quantification for features of substantive scientific interest, such as the posterior probability of inclusion of selected edges, or paths.
A Hybrid TLBO Algorithm by Quadratic Approximation for Function Optimization and Its Application

Recently hybrid optimization algorithms enjoy growing attention in the optimization community. However, over the last two decades, many new hybrid meta-heuristics optimization techniques are developed and are still developing. On the hybrid optimization algorithm, the most common criticism is that they are not well balanced in respect of the local search and global search of the algorithm. Viewing this, in the present work a modified adaptive based teaching factor is suggested for the basic TLBO algorithm. Also, a novel hybrid approach is proposed that combines the Teaching Learning Base Optimization (TLBO) Algorithm and Quadratic approximation (QA). The QA is applied to improve the global as well as local search capability of the method that also represents the characters of “Teacher Refresh”. For the performance investigation, the suggested algorithm is involved to solve twenty classical optimization functions and one real life optimization problem and the performances are differentiated with different state-of-the-arts methods in terms of numerical results of the solution.
Aggregation Strategies in User-to-User Reciprocal Recommender Systems

Reciprocal Recommender Systems are a class of Recommender System for recommending people to people, where the preference relationship is bidirectional and both sides must be considered in the recommendation process. Reciprocal recommender systems often generate preference values using similar algorithms to traditional recommender systems, and then they combine two preference scores into a single indicator of the compatability between the two users. None of the few existing approaches for reciprocal recommendation have investigated the influence of different aggregation functions in these systems as of yet. Our work concentrates on exploring diverse approaches for aggregating bidirectional preference relations, and presents the results of a comparative study.
Online Social Shaming and the Moralistic Imagination: The Emergence of Internet‐Based Performative Shaming

Online social shaming involves the intentional collection and dissemination of data that are potentially stigmatizing in modes that are widely accessible and in which observers (including members of the public) can often add input. This article examines changes in shaming following the advent of the widespread use of computer networking, focusing in particular on administrative systems. Many of the Internet platforms, in which such shaming‐related information is made available is either controlled by or used in governmental and agency units to perform various stated administrative or evaluatory purposes. Although many shaming contexts are inadvertent (a function of public records release), others are developed with the use of “moralistic imaginations,” with implementers and other participants inventively linking specific activities with perceived moral deficiencies within particular settings. The article outlines an assortment of “performative shaming” approaches using these platforms, with digital vigilantism (sometimes labeled as “digilantism” or “netilantism”) often involved or even fostered in some online shaming initiatives. It highlights the cases of shamings related to commonplace contexts such as school lunch money deficits and reported teacher productivity dysfunctions, as well as tax and child support delinquencies, opening questions of how everyday life activities are construed and reframed in the process.
Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization

Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitching methods based on hand-crafted features are effective for constructing a cylindrical panorama from a sequence of images in the case when there are sufficient reliable features in the scene. However, these methods are unable to handle low-texture environments where no reliable feature correspondence can be established. This paper proposes a novel two-step image alignment method based on deep learning and iterative optimization to address the above issue. In particular, a light-weight end-to-end trainable convolutional neural network (CNN) architecture called ShiftNet is proposed to estimate the initial shifts between images, which is further optimized in a sub-pixel refinement procedure based on a specified camera motion model. Extensive experiments on a synthetic dataset, rendered photo-realistic images, and real images were carried out to evaluate the performance of our proposed method. Both qualitative and quantitative experimental results demonstrate that cylindrical panorama stitching based on our proposed image alignment method leads to significant improvements over traditional feature based methods and recent deep learning based methods for challenging low-texture environments.
Interpretable Feature Generation using Deep Neural Networks and its Application to Lane Change Detection

This paper proposes a novel approach towards generating interpretable features from time series using deep neural networks. It is shown that by exploiting the advantages of heatmapping techniques, the dimensions of an interpretable domain can be strongly reduced while introducing new and relevant features to the dataset. It is then possible to learn interpretable classifiers with these features. The proposed method is a combined feature generation and selection approach that increases accuracy as compared to reference techniques while remaining fully interpretable. The methodology uses the fact that the ouput of the first convolutional layer can preserve interpretability in time series data. The application of classifying lane change intentions is considered and the public dataset highD is used to validate the approach. The results show that the generated features yield better classification results than compared reference feature sets, thereby proving the high relevance of the features that were generated with the help of deep neural networks. Especially the interpretable binary decision tree classifier shows a strong improvement in accuracy when trained on the generated feature set, providing a powerful and from input to output interpretable classification method in combination.