Reviewerly/exHarmony huggingface dataset:
A taxonomy of software-based and hardware-based approaches for energy efficiency management in the Hadoop

Apache Hadoop framework supports the storing and processing of big data datasets using simple programming models. Energy management has been recognized as one of the major issues in Hadoop, and many types of research have been conducted in this scope. However, despite the importance of this issue, there is no inclusive study about energy efficiency in Hadoop. In this paper, the techniques of energy efficiency in Hadoop are classified into two main categories. Moreover, the benefits and drawbacks of these methods and a systematic study of the conducted research are provided and examined in this paper. Another aim is to provide the visions for the descriptions of open issues and recommendations for future research.
Protecting User Privacy

The overturning of the Internet Privacy Rules by the Federal Communications Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs) to collect, share and sell their customers' Web browsing data without their consent. With third-party trackers embedded on Web pages, this new rule has put user privacy under more risk. The need arises for users on their own to protect their Web browsing history from any potential adversaries. Although some available solutions such as Tor, VPN, and HTTPS can help users conceal their online activities, their use can also significantly hamper personalized online services, i.e., degraded utility. In this paper, we design an effective Web browsing history anonymization scheme, PBooster, aiming to protect users' privacy while retaining the utility of their Web browsing history. The proposed model pollutes users' Web browsing history by automatically inferring how many and what links should be added to the history while addressing the utility-privacy trade-off challenge. We conduct experiments to validate the quality of the manipulated Web browsing history and examine the robustness of the proposed approach for user privacy protection.
Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.
Attention With Sparsity Regularization for Neural Machine Translation and Summarization

The attention mechanism has become the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de facto</i> standard component in neural sequence to sequence tasks, such as machine translation and abstractive summarization. It dynamically determines which parts in the input sentence should be focused on when generating each word in the output sequence. Ideally, only few relevant input words should be attended to at each decoding time step and the attention weight distribution should be sparse and sharp. However, previous methods have no good mechanism to control this attention weight distribution. In this paper, we propose a sparse attention model in which a sparsity regularization term is designed to augment the objective function. We explore two kinds of regularizations: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{\infty }$</tex-math></inline-formula> -norm regularization and minimum entropy regularization, both of which aim to sharpen the attention weight distribution. Extensive experiments on both neural machine translation and abstractive summarization demonstrate that our proposed sparse attention model can substantially outperform the strong baselines. And the detailed analyses reveal that the final attention distribution indeed becomes sparse and sharp.
Fast Single-Image Super-Resolution via Deep Network With Component Learning

Driven by the spectacular success of deep learning, several advanced models based on neural networks have recently been proposed for single-image super-resolution, incrementally revealing their superiority over their alternatives. In this paper, we pursue this latest line of research and present an improved network structure by taking advantage of the proposed component learning. The core idea and difference of this learning strategy are to use the residual extracted from the input to predict its counterpart in the corresponding output. To this end, a global decomposition procedure is designed on the basis of convolutional sparse coding and performed on the input for extracting the low-resolution (LR) residual component from it. Owing to the properties of this decomposition, the represented residual component still stays in the LR space so that the subsequent part is capable of operating it economically in terms of computational complexity. Thorough experimental results demonstrate the merit and effectiveness of the proposed component learning strategy, and our trained model outperforms many state-of-the-art methods in terms of both speed and reconstruction quality.
Experiences of undergraduate nursing students on an authentic mobile learning enactment at a higher education institution in South Africa

Mobile technology has infiltrated our day to day existence through provision of inexhaustible access to communication and information. In education, mobile devices are not only used as tools to reinforce information, motivate and accentuate engagement, but it additionally enables the delivery of course content. The adoption of authentic technological innovations using the variety of distinguishing attributes available on mobile devices could potentially promote a mobile learning enactment.The purpose of the study was to explore the experiences of undergraduate nursing students who participated in an authentic mobile learning enactment aimed at enhancing their learning experiences.This study used a qualitative contextual design.Undergraduate nursing students, of a School of Nursing in a Faculty of Community and Health Sciences at a university in South Africa, were the study participants. All students (n = 101) registered for the primary care and clinical skills module were invited to submit reflections based on their experiences on an authentic mobile learning enactment using WhatsApp Messenger.Students submitted electronic reflections after every two-week cycle of the enactment. The data collected was categorised into emerging themes as analysed by the researcher guided by Tesch's (1990) systematic process. An independent coder reviewed the data and, through consensus, themes identified were confirmed.One hundred and one participants (n = 101; 100%) submitted online reflections on their experiences during the authentic mobile learning enactment. Seven themes were identified which included: mobile devices afforded a learning platform; mobile learning enactment enhanced engagement; learning within a group made learning easier; flexibility in time allocated to complete tasks; challenges experienced with data/airtime/Wi-Fi; impaired communication due to poor network access and use of mobile devices in practice perceived as unprofessional.The study provided valuable insights into students' experiences of the authentic mobile learning enactment, as well as suggesting ways to enhance the effectiveness of such an enactment.
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds

This paper presents a novel deep learning framework for human trajectory prediction and detecting social group membership in crowds. We introduce a generative adversarial pipeline which preserves the spatio-temporal structure of the pedestrian’s neighbourhood, enabling us to extract relevant attributes describing their social identity. We formulate the group detection task as an unsupervised learning problem, obviating the need for supervised learning of group memberships via hand labeled databases, allowing us to directly employ the proposed framework in different surveillance settings. We evaluate the proposed trajectory prediction and group detection frameworks on multiple public benchmarks, and for both tasks the proposed method demonstrates its capability to better anticipate human sociological behaviour compared to the existing state-of-the-art methods (This research was supported by the Australian Research Council’s Linkage Project LP140100282 “Improving Productivity and Efficiency of Australian Airports”).
Scan and Learn: Quick Response Code Enabled Museum for Mobile Learning of Anatomy and Pathology

In the past, medical museums played a significant role in anatomy and pathology training. The attraction of medical museums has declined recently due to the emergence of information technology and innovative medical curricula. An innovative mobile learning platform has been developed using quick response (QR) codes for the museum specimens at the Lee Kong Chain School of Medicine, Singapore. High‐quality images of the potted specimens were captured and combined into an album and a video using Adobe Acrobat Pro 9 and Windows Movie Maker, respectively. Subsequently, QR codes were generated linking to PDF documents with annotations, pathology, and clinical history concerning the specimens. Quick response codes were piloted in gastrointestinal teaching module for Year 2 medical students. Survey responses were obtained from students to verify the efficacy of QR as a learning tool. The majority of students either agreed or strongly agreed that it was easy to access the information about the specimen with QR codes (4.47 ± 0.84), while 96% of students agreed that they are able to correlate the specimen with the annotated images (4.56 ± 0.56). The majority of students (78%) agreed that QR codes are useful for their learning (4.22 ± 0.87), while 75% of students felt QR codes motivate them to visit Anatomy Resource Centre. Most of the students agreed that QR codes are useful for revision of materials (4.13 ± 1.07) and independent learning (4.38 ± 0.87). These findings suggest that QR codes are not only effective for students learning but also enhance their exploration experience with the museum specimens.
Predicting Stereoscopic Image Quality via Stacked Auto-Encoders Based on Stereopsis Formation

Most previous 2-D and 3-D image quality evaluators were based on shallow architectures. Their shallow architectures cannot model phenomenon occurring in human visual systems sufficiently. Disparities between left and right views have been importantly used for 3-D image quality assessment (IQA), but single disparity, depth, or cyclopean maps made from the disparities cannot thoroughly reflect the depth sense. In this paper, we propose a blind stereoscopic image quality evaluator using stacked auto-encoders (SAE). The proposed method is based on two theories on initial stages of stereopsis. One is cyclopean channel theory and the other is binocular summation/difference channels theory. Especially, a cyclopean image that models the former theory is computed to consider binocular suppression, whereas summation and difference images that model the latter one are utilized to treat the depth sense. We train three SAEs for quality-aware features from the three images in an unsupervised manner. Through the SAEs, the features are transformed into more meaningful features, and they are used to train two regressors. The regressors are used to obtain a final predicted score. Experimental results conducted on popular 3-D IQA databases prove that the proposed algorithm outperforms state-of-the-art 3-D IQA methods.
Group preserving label embedding for multi-label classification

Multi-label learning is concerned with the classification of data with multiple class labels. This is in contrast to the traditional classification problem where every data instance has a single label. Due to the exponential size of output space, exploiting intrinsic information in feature and label spaces has been the major thrust of research in recent years and use of parametrization and embedding have been the prime focus. Researchers have studied several aspects of embedding which include label embedding, input embedding, dimensionality reduction and feature selection. These approaches differ from one another in their capability to capture other intrinsic properties such as label correlation, local invariance etc. We assume here that the input data form groups and as a result, the label matrix exhibits a sparsity pattern and hence the labels corresponding to objects in the same group have similar sparsity. In this paper, we study the embedding of labels together with the group information with an objective to build an efficient multi-label classifier. We assume the existence of a low-dimensional space onto which the feature vectors and label vectors can be embedded. In order to achieve this, we address three sub-problems namely; (1) Identification of groups of labels; (2) Embedding of label vectors to a low rank-space so that the sparsity characteristic of individual groups remains invariant; and (3) Determining a linear mapping that embeds the feature vectors onto the same set of points, as in stage 2, in the low-dimensional space. We compare our method with seven well-known algorithms on twelve benchmark data sets. Our experimental analysis manifests the superiority of our proposed method over state-of-art algorithms for multi-label learning.
E2SAM: Evolutionary ensemble of sentiment analysis methods for domain adaptation

Currently, a plethora of industrial and academic sentiment analysis methods for classifying the opinion polarity of a text are available and ready to use. However, each of those methods have their strengths and weaknesses, due mainly to the approach followed in their design (supervised/unsupervised) or the domain of text used in their development. The weaknesses are usually related to the capacity of generalisation of machine learning algorithms, and the lexical coverage of linguistic resources. Those issues are two of the main causes of one of the challenges of Sentiment Analysis, namely the domain adaptation problem. We argue that the right ensemble of a set of heterogeneous Sentiment Analysis Methods will lessen the domain adaptation problem. Thus, we propose a new methodology for optimising the contribution of a set of off-the-shelf Sentiment Analysis Methods in an ensemble classifier depending on the domain of the input text. The results clearly show that our claim holds.
Real-time Secure Health Surveillance for Smarter Health Communities

Pervasive healthcare services with smart decision making capability and ubiquitous communication technologies can forge future smart communities. Real-time health surveillance for early detection of life-threatening diseases through advanced sensing and communication technology can provide better treatment, reduce medical expenses and save lives of community residents (i.e., patients). However, the assurance of data privacy is the prime concern for such smart health technologies. This research aims to describe a privacy-preserving cloud-based system for real-time health surveillance through change detection of multiple vital health signs of smart community members. Vital signs data generated from IoT-enabled wearable devices are processed in real-time in a cloud environment. This article focuses on the development of a predictive model for the smart community considering the sensitivity of data processing in a third-party environment (e.g., cloud computing). We developed a vital sign change detection system using Holt's linear trend method (to enable prediction of data with trends) where fully homomorphic encryption is adapted to perform computations on an encrypted domain that can ensure data privacy. Moreover, to reduce the overhead of the fully homomorphic encryption method over large medical data we introduced a parallel approach for encrypted computations using a MapReduce algorithm of Apache Hadoop. We demonstrated the proposed model by evaluating some case studies for different vital signs of patients. The accuracy and efficiency of the implementation demonstrate the effectiveness of the proposed model for building a smart community.
Unsupervised fuzzy model-based Gaussian clustering

In 1993, Banfield and Raftery first proposed model-based Gaussian (MB-Gauss) clustering, using eigenvalue decomposition of Gaussian covariance matrix to detect different cluster shapes. In this paper, we extend MB-Gauss to a fuzzy model-based Gaussian (F-MB-Gauss) clustering. However, the performance of both MB-Gauss and F-MB-Gauss algorithms depend heavily on initializations and require a number of clusters to be assigned a priori. To solve these problems, an unsupervised learning schema for F-MB-Gauss clustering is proposed. Therefore, an unsupervised fuzzy model-based Gaussian (UF-MB-Gauss) clustering algorithm is constructed where the proposed UF-MB-Gauss algorithm not only solves the initialization problem, but can also automatically obtain an optimal number of clusters. In the literature, MB-Gauss with Bayesian information criterion (BIC) is a common way for determining the number of clusters for Gaussian mixtures. Furthermore, several clustering methods that can automatically determine the number of clusters have been proposed in the literature. Therefore, comparison is made between the proposed UF-MB-Gauss clustering algorithm and these related clustering methods using numerical data and real data sets. Experimental results and comparisons demonstrate the superiority and usefulness of the proposed UF-MB-Gauss clustering algorithm.
Hybrid pooling for enhancement of generalization ability in deep convolutional neural networks

Convolutional neural networks (CNNs) have attracted considerable attention in many application fields for their great ability to deal with image recognition and object detection tasks. A pooling process is an important process in CNNs, which serves to decrease the dimensionality of processed data for reducing computational cost as well as for enhancing tolerance to translation and noise. Although standard pooling methods, such as the max pooling and the average pooling, are typically adopted in many studies, a newly devised pooling method could improve the generalization ability of CNNs. In this study, we propose a hybrid pooling method which stochastically chooses the max pooling or the average pooling in each pooling layer. A characteristic of the hybrid pooling is that the probability for choosing one of the two pooling methods can be controlled for each convolutional layer. In image classification tasks with benchmark datasets, we show that the hybrid pooling is effective for increasing the generalization ability of CNNs. Moreover, we demonstrate that the hybrid pooling combined with the dropout is competitive with other existing methods in classification performance.
Hyperspectral‐based Inversion of Heavy Metal Content in the Soil of Coal Mining Areas

Coal mining has environmental impacts on surrounding areas, including heavy metal contamination of soil. This study explores the feasibility of using hyperspectral remote sensing to determine the heavy metal (Cr, Ni, Cu, Zn, Cd, Pb) content of soils in a coal-mining area in the city of Zoucheng, Shandong Province, China. We used a plasma mass spectrometer to measure the heavy metal contents of soils and an ASD Field Spec4 spectrometer to measure soil hyperspectral data. Savitzky-Golay (SG) convolution smoothing and multiplicative scatter correction (MSC) were applied to the data, along with multiple mathematical transformations. Finally, a regression model for estimating heavy metal content of soils was developed using partial least squares regression (PLSR) analysis. Results show that the average heavy metal content of study soils was lower than the national standard value of soil environmental quality. The model's predictive accuracy is extremely high for Ni ( = 0.923 and RMSE = 0.831 by modeling; = 0.879 and RMSE = 1.292 by testing); ideal for Cr, Cu, Zn, and Pb; and insufficient for Cd. Preprocessing the reflectance spectra with SG convolution smoothing in combination with MSC and reciprocal logarithm transformation yields the highest model accuracy. Hyperspectral PLSR modeling can effectively predict heavy metal content of soils in coal-mining areas, and preprocessing spectral data is crucial for achieving high prediction accuracy.
Ship Detection in SAR Images Based on Maxtree Representation and Graph Signal Processing

This paper discusses an image processing architecture and tools to address the problem of ship detection in synthetic-aperture radar images. The detection strategy relies on a tree-based representation of images, here a Maxtree, and graph signal processing tools. Radiometric as well as geometric attributes are evaluated and associated with the Maxtree nodes. They form graph attribute signals which are processed with graph filters. The goal of this filtering step is to exploit the correlation existing between attribute values on neighboring tree nodes. Considering that trees are specific graphs where the connectivity toward ancestors and descendants may have a different meaning, we analyze several linear, nonlinear, and morphological filtering strategies. Beside graph filters, two new filtering notions emerge from this analysis: tree and branch filters. Finally, we discuss a ship detection architecture that involves graph signal filters and machine learning tools. This architecture demonstrates the interest of applying graph signal processing tools on the tree-based representation of images and of going beyond classical graph filters. The resulting approach significantly outperforms state-of-the-art algorithms. Finally, a MATLAB toolbox allowing users to experiment with the tools discussed in this paper on Maxtree or Mintree has been created and made public.