Reviewerly/exHarmony huggingface dataset:
Parallel implementation of the Image Block Representation using OpenMP

Herein, a parallel implementation in OpenMP of the Image Block Representation (IBR) for binary imagesis investigated. The IBR is a region-based image representation scheme that represents the binary image as a set of non-overlapping rectangular areas with object level, called blocks. The IBR permits the execution of operations on image areas instead of image points and therefore leads to a substantial reduction of the required computational complexity. The experimental and the analytically derived results from parallel implementation in OpenMP, on a multicore computer, proved that a very good overall performance can be achieved.
A Multimodal Approach to Image Sentiment Analysis

Multimodal sentiment analysis is a process for the classification of the content of composite comments in social media at the sentiment level that takes into consideration not just the textual content but also the accompanying images. A composite comment is normally represented by the union of text and image. Multimodal sentiment analysis has a great dependency on text to obtain its classification, because image analysis can be very subjective according to the context where the image is inserted. In this paper we propose a method that reduces the text analysis dependency on this kind of classification giving more importance to the image content. Our method is divided into three main parts: a text analysis method that was adapted to the task, an image classifier tuned with the dataset that we use, and a method that analyses the class content of an image and checks the probability that it belongs to one of the possible classes. Finally a weighted sum takes the results of these methods into account to classify content according to its sentiment class. We improved the accuracy on the dataset used by more than 9%.
A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification

Learning interpretable data representation has been an active research topic in deep learning and computer vision. While representation disentanglement is an effective technique for addressing this task, existing works cannot easily handle the problems in which manipulating and recognizing data across multiple domains are desirable. In this paper, we present a unified network architecture of Multi-domain and Multi-modal Representation Disentangler (M2RD), with the goal of learning domain-invariant content representation with the associated domain-specific representation observed. By advancing adversarial learning and disentanglement techniques, the proposed model is able to perform continuous image manipulation across data domains with multiple modalities. More importantly, the resulting domain-invariant feature representation can be applied for unsupervised domain adaptation. Finally, our quantitative and qualitative results would confirm the effectiveness and robustness of the proposed model over state-of-the-art methods on the above tasks.
Transfer Learning for Facial Attributes Prediction and Clustering

Notwithstanding the enhancement obtained in the last decade researches, the recognition of facial attributes is still today a trend. Besides the mere face recognition, the singular face features, like mouth, nose and hair, are considered as soft biometrics; these can be useful for human identification in cases the face is partially occluded, and only some regions are visible. In this paper we propose a model generated by transfer learning approach for the recognition of the face attributes. Also, an unsupervised clustering model is described, which is in charge of dividing and grouping faces based on their characteristics. Furthermore, we show how clusters can be evaluated by a compact summary of them, and how Deep Learning models should be properly trained for attribute prediction tasks.
Sentiment Analysis of Tweets Using Supervised Learning Algorithms

The proliferation of user-generated content (UGC) on social media platforms has made user opinion tracking a strenuous job. Twitter, being a huge microblogging social network, could be used to accumulate views about politics, trends, and products, etc. Sentiment analysis is a mining technique employed to peruse opinions, emotions, and attitude of people toward any subject. This is conceptualized using digital data (text, video, audio, etc.) or psychological characteristics of humans. This procedure assists in opinion mining without having to read a plethora of tweets manually. The results could be wielded to provide an edge for businesses and governments in rolling out new entities (policies, products, topic, event). Cleaning data is an important step here, which we accomplished using regular expressions and NLTK library in Python. We implemented nine separate algorithms to classify tweets and compare their performance on cleaned data. It was observed that the convolutional neural network produces the most optimal results at 79% accuracy.
ARSpectator: Exploring Augmented Reality for Sport Events

Augmented Reality (AR) has gained a lot of interests recently and has been used for various applications. Most of these applications are however limited to small indoor environments. Despite the wide range of large scale application areas that could highly benefit from AR usage, until now there are rarely AR applications that target such environments. In this work, we discuss how AR can be used to enhance the experience of on-site spectators at live sport events. We investigate the challenges that come with applying AR for such a large scale environment and explore state-of-the-art technology and its suitability for an on-site AR spectator experience. We also present a concept design and explore the options to implement AR applications inside large scale environments.
LSTM With Adam Optimization-Powered High Accuracy Preeclampsia Classification

Preeclampsia is a pregnancy complication characterized by high blood pressure and signs of damage to another organ system, most often the liver and kidneys. Preeclampsia usually begins after 20 weeks of pregnancy in women whose blood pressure had been normal. To anticipate the risk of preeclampsia and prevent the death of pregnant women, a predictive system is needed for preeclampsia. to predict this, the data obtained will be studied to recognize patterns for predicting preeclampsia. Artificial neural network is one method that has the ability to learn patterns from a data. The system built in this study is to use the artificial neural network method by using the architecture of Long Short-Term Memory (LSTM) Neural Networks. But this technique requires the right parameters to get accurate predictions. In this paper analyze several parameters such as number of time series patterns, number of hidden neurons, max epoch, and composition of training and test data on the accuracy of the predictions obtained. The results of the analysis showed that the system built was able to predict Preeclampsia well, with an average accuracy rate of 90.22% for data testing.
Reconstruction of quantum channel via convex optimization

Quantum process tomography is often used to completely characterize an unknown quantum process. However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimum that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Here, we reconstruct the seawater channel using convex optimization and further test it on the seven fundamental gates. We compare our method to the standard-inversion and norm-optimization approaches using the cost function value and our proposed state deviation. The advantages convince that our method enables a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources. In addition, we examine on a set of non-unitary channels and the reconstructions reach up to 99.5% accuracy. Our method offers a more universal tool for further analyses on the components of the quantum channels and we believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.
Safe Enough to Share

CSCW research is increasingly interested in the ways that people use technology to discuss health and disability online. In addition to studying how people share information and seek and provide emotional support, a growing area of interest is health activism. In this paper, we analyze how a project centered around sharing "real and raw" experiences with dementia provides a safe platform for people to share their authentic experiences. These accounts counter predominant depictions of dementia and push back on tokenistic involvement of people with this condition. In a study involving observations and interviews with members of this project, we find that people with dementia must negotiate several goals which at times compete with each other: sharing a "real and raw" look at dementia, changing attitudes, showcasing a polished presentation, and inhabiting a safe space. The paper concludes with a discussion of future directions for CSCW on configuring a space for dialogue on sensitive topics, health activism, and sharing online with dementia.
Fuzzy Identity-Based Dynamic Auditing of Big Data on Cloud Storage

To ensure the reliability and integrity of data in the cloud storage server, some scholars provided various data integrity auditing schemes.However, the most existing data integrity auditing schemes only support the static data and may be unsuitable for the dynamic operations of data.To overcome this difficulty, we propose a fuzzy identity-based dynamic auditing of big data, which combines the structure of the Merkle hash tree (MHT) with the Index logic table (ILT).Our scheme not only performs the dynamic operations of data block in the ILT, namely modification, insertion and deletion, but also efficiently executes dynamic operations of the ILT on the structure of the MHT.We also elaborate the security, characteristics and performance analysis of the proposed scheme separately.The analysis results show that the proposed scheme costs less time than the structure of the original MHT to generate the root node hash value during the metadata generation phase and update the root node hash value during the dynamic operations.Furthermore, when users store the new ILT in local storage, they require lower communication cost to update root node hash value than users without storing the ILT, and fewer interactions between the cloud storage server and users in the dynamic operations process.
Medical Image Segmentation Using Deep Learning

This chapter aims at providing an introduction to deep learning-based medical image segmentation. First, the reader is guided through the inherent challenges of medical image segmentation, for which actual approaches to overcome those limitations are discussed. Secondly, supervised and semi-supervised architectures are described, where encoder-decoder type networks are the most widely employed ones. Nonetheless, generative adversarial network-based semi-supervised approaches have recently gained the attention of the scientific community. The shift from traditional 2D to 3D architectures is also discussed, as well as the most common loss functions to improve the performance of medical image segmentation approaches. Finally, some future trends and conclusion are described.
Big Data and Cloud Computing

Abstract Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processing, analytics, visualization, sharing, and applications. During the same time frame, cloud computing emerged to provide crucial computing support to address these challenges. This chapter introduces Digital Earth data sources, analytical methods, and architecture for data analysis and describes how cloud computing supports big data processing in the context of Digital Earth.
A Siamese Pedestrian Alignment Network for Person Re-identification

Deep learning methods show strong ability in extracting high-level features for images in the field of person re-identification. The produced features help inherently distinguish pedestrian identities in images. However, on deep learning models over-fitting and discriminative ability of the learnt features are still challenges for person re-identification. To alleviate model over-fitting and further enhance the discriminative ability of the learnt features, we propose siamese pedestrian alignment networks (SPAN) for person re-identification. SPAN employs two streams of PAN (pedestrian alignment networks) to increase the size of network inputs over limited training samples and effectively alleviate network over-fitting in learning. In addition, a verification loss is constructed between the two PANs to adjust the relative distance of two input pedestrians of the same or different identities in the learned feature space. Experimental verification is conducted on six large person re-identification datasets and the experimental results demonstrate the effectiveness of the proposed SPAN for person re-identification.
Session-based Recommendation with Hierarchical Memory Networks

The task of session-based recommendation aims to predict users' future interests based on anonymous historical sessions. Recent works have shown that memory models, which capture user preference from previous interaction sequence with long short-term or short-term memory, can lead to encouraging results in this problem. However, most existing memory models tend to regard each item as a memory unit, which neglect n-gram features and are insufficient to learn the user's feature-level preferences. In this paper, we aim to leverage n-gram features and model users' feature-level preferences in an explicit and effective manner. To this end, we present a memory model with multi-scale feature memory for session-based recommendation. A densely connected convolutional neural network (CNN) with short-cut path between upstream and downstream convolutional blocks is applied to build multi-scale features from item representations, and features in the same scale are combined with memory mechanism to capture users' feature-level preferences. Furthermore, attention is used to adaptively select users' multi-scale feature-level preferences for recommendation. Extensive experiments conducted on two benchmark datasets demonstrate the effectiveness of the proposed model in comparison with competitive baselines.
Pruning 3D Filters For Accelerating 3D ConvNets

Many methods have been proposed to accelerate 2D ConvNets by removing redundant parameters. However, few efforts are devoted to the problem of accelerating 3D Convolutional Networks. The 3D ConvNets, which are mainly designed for extracting spatiotemporal features, have been widely used in many video analytics tasks, such as action recognition and scene analysis. In this paper, we focus on accelerating 3D ConvNets for two motivations: (1) Fast video processing techniques are in dire need due to the explosive growth of video data; (2) Compared with individual images, video data consist of consecutively similar frames, thus are inherently more redundant. In this paper, we present a novel algorithm to dramatically accelerate 3D ConvNets by pruning redundant convolutional filters, while preserving the discriminative power of the networks. Specifically, we formulate the filter pruning from 3D ConvNets as a subset selection problem where each filter is regarded as a candidate. Determinantal Point Processes (DPPs) are employed to discriminatively select the filter candidates which are informative and yet diverse. We evaluate our method using two popular 3D networks, C3D and Pseudo-3D, on Sports-1 M dataset for video classification. Extensive experimental results demonstrate both the efficiency and performance advantages of our method. We also show that the proposed method can be easily generalized to 2D ConvNets pruning with promising experimental results on VGGnet and ResNet.
Information and Education Systems in the Context of Digitalization of Education

The article describes the changes in the modern socio-economic environment, which affect the digitalization of society, including the socio-educational sphere. On the basis of the authors’ proposals for the development of the concept of “information-educational systems”, mathematical models for their description and mechanisms for analysing their essential characteristics determined by modern processes of development and digitalization of society are indicated. Through the example of using elements of the theory of dynamic deductive databases and the theory of neural networks, the article describes mathematical models that adequately reflect strategies for the development of people and stable small groups, as well as large dynamic human communities, on the basis of which recommendations are proposed for developing such strategies. The results can be used in the pedagogical design of various models of educational activities and information and educational systems for various purposes, including education management and direct training. It also seems that the mechanisms and features of information and educational systems described in the article will allow us to establish an evidence-based educational policy at various levels in the context of digitalization
Virtual Reality Forge: Pattern-Oriented Authoring of Virtual Reality Nuggets

A current educational trend is to divide learning content in relatively small and independent learning units, referred to as learning nuggets. These “bite-sized” nuggets often rely on patterns in order to reuse these patterns within highly diverse curricular structures like lessons, presentations or demos.
Hybridizing Teaching-Learning-Based Optimization With Adaptive Grasshopper Optimization Algorithm for Abrupt Motion Tracking

Aiming at the problem that conventional tracking algorithms are difficult to deal with abrupt motion efficiently, an optimization algorithm called hybrid Teaching-learning-based optimization with Adaptive Grasshopper Optimization Algorithm (TLGOA) is proposed in this paper.Firstly, the non-linear strategy based on tangent function is used to replace the linear mechanism in the standard Grasshopper Optimization Algorithm (GOA).The improved adaptive GOA (AGOA) can avoid the local trapping problem and enhance the global optimization ability, which can handle the problem of abrupt motion.Secondly, considering that Teaching-learning-based optimization (TLBO) has obviously local exploitation operator and fast convergence, a hybrid TLGOA tracker is designed by combining the advantages of both AGOA and TLBO.The approach can enable better tracking accuracy and efficiency.Finally, extensive experimental results show that the proposed algorithm has obvious advantages over other algorithms, and also prove that TLGOA tracker is very competitive compared to other state-of-the-art trackers, especially for abrupt motion tracking.
An Evaluation of Parametric Activation Functions for Deep Learning

Parametric activation functions, such as PReLU and PELU, are a relatively new subdomain of neural network nonlinearities. In this paper, we present a comparison of these methods across several topologies. We find that parameterizing activation functions in neural networks do not tend to overfit and tend to converge more quickly than non-parametric activations. This is especially important in environments where time and resources are limited, such as in embedded and mobile systems. We also introduce the Bendable Linear Unit (BLU), which synthesizes many useful properties of other activations, including PReLU, ELU, and SELU. Our experiments indicate that parametric activations that can approximate the identity function can autonomously learn to make residual connections in deep networks. BLU outperforms other activations on the CIFAR -10 task when using a topology without explicit residual connections. BLU also achieves the highest predictive accuracy of compared activations on the CIFAR -100 task when training with a time limit.
An Earthquake Early Warning Method Based on Huygens Principle: Robust Ground Motion Prediction Using Various Localized Distance‐Attenuation Models

Abstract For the stable operation of earthquake early warning systems under complex earthquake scenarios such as large earthquakes with complicated rupture processes and multiple simultaneous events during intense aftershock activity, wavefield‐based ground motion prediction approaches that can predict shaking directly from the observed shaking, without source parameter characterization, have been proposed. These existing algorithms, however, have weak points, including the employment of inflexible wave propagation models and limited available lead times for sites that have a potential to obtain long lead times. To address these shortcomings, I developed the Approximation by Local Pseudo‐Hypocenter Attenuation (ALPHA) method, a new wavefield‐based algorithm that predicts ground motion by estimating the distance‐attenuation relation from direct observations of the ongoing wavefield in real time. For this estimation, ALPHA uses multiple point‐source models located below each observation station to simulate elementary wave propagation in accordance with Huygens principle. Application to the 2011 M w 9.0 Tohoku‐Oki and 2016 M 7.3 Kumamoto earthquakes and 21 additional earthquakes with M ≥ 7 showed that ALPHA stably calculated accurate ground motion predictions for those events and provided long lead times for sites with an intensity threshold on bedrock of three on the Japan Meteorological Agency scale (~IV–VI on the modified Mercalli intensity scale). These findings indicate that ALPHA would enable early warning systems to issue accurate warnings to wider areas at an early stage than existing algorithms.
A method of solving a nonlinear boundary value problem with a parameter for a loaded differential equation

A nonlinear loaded differential equation with a parameter on a finite interval is studied. The interval is partitioned by the load points, at which the values of the solution to the equation are set as additional parameters. A nonlinear boundary value problem for the considered equation is reduced to a nonlinear multipoint boundary value problem for the system of nonlinear ordinary differential equations with parameters. For fixed parameters, we obtain the Cauchy problems for ordinary differential equations on the subintervals. Substituting the values of the solutions to these problems into the boundary condition and continuity conditions at the partition points, we compose a system of nonlinear algebraic equations in parameters. A method of solving the boundary value problem with a parameter is proposed. The method is based on finding the solution to the system of nonlinear algebraic equations composed.