Reviewerly/exHarmony huggingface dataset:
Profile clustering in clinical trials with longitudinal and functional data methods

Repeated measurements are widely encountered in medical or pharmaceutical studies, which can be analyzed by both longitudinal data and functional data analysis methods, particularly when the underlying process is continuous and the number of measurement points is not too small. Motivated by real problems of clustering patient profiles in clinical trials, this paper gives an overview of the clustering methods for repeated measurement data and compares three longitudinal data methods and two functional data methods with extensive simulation studies. Methods with appropriate properties are applied to the real data to produce interpretable results.
Enhancing Learning and Teaching for Architectural Engineering Students uing Virtual Building Design and Construction

It is important for students in the built environment related disciplines to acquire subject-based knowledge and skills from reflecting on their experience. In return, the learning-by-doing approach has been widely adopted in the academic cluster of built environment. To further strengthen this, this paper proposes a Virtual Reality (VR) based learning and teaching tool. It creates a virtual 3D environment that helps architectural engineering students conceive their design ideas, plan the layout, design the structure, construct the products (buildings, communities, infrastructures, etc.), and directly interact with the products they designed. The objectives of this research are: (1) to build a VR design environment for students to experience corresponding impact from different scenarios, which will help the student understand and investigate different design theories and schemes; (2) to build a VR construction environment for students to investigate how the building is built and what safety issues should be noted when visiting a construction site; and (3) to provide a collaborative environment for students in the Built Environment domain for better communication through a complete building project featuring active and experiential learning. Unity is used to develop the package and VIVE, a VR package, is used to facilitate the immersive interaction between the virtual environment and the users. Students from the Built Environment cluster were invited to use the tool and give feedback using a questionnaire. Positive comments were given by the students showing that they were very interested in studying academic subjects through such a technical game. All of them wanted to play more rounds to improve their performance and to find answers to the questions they failed to answer correctly. Most of the students were willing to spend more time in finding answers after playing that game.
Allocation of energy-efficient task in cloud using DVFS

Nowadays, the expanding computational capabilities of the cloud system rely on the minimisation of the consumed power to make them sustainable and economically productive. Power management of cloud data centres received a great attention from industry and academia as it consumes high energy and thus increases the operational cost. One of the core approaches for the conservation of energy in the cloud data centre is the task scheduling. This task allocation in a heterogeneous environment is a well known NP-hard problem due to which researchers pay attention for proposing various heuristic techniques for the problem. In this paper, a technique is proposed based on dynamic voltage frequency scaling (DVFS) for optimising the energy consumption in the cloud environment. The basic idea is to address the trade-off between energy consumption and makespan of the system. Here, we formally introduce a model that includes various subsystems and assess the implementation of the algorithm in the heterogeneous environment.
State of Social Media, Middle East: 2018

This report is the seventh in an annual series of publications showcasing the latest developments, trends and research in social media usage across the Middle East and North Africa (MENA). Notable trends from the past year include: the continued popularity of Facebook and Facebook-owned products, especially with Arab Youth, as well as increasing challenges to online freedom of expression in many parts of the region. Saudi Arabia continues to be a social media powerhouse, being one of the biggest national markets for Snapchat and YouTube in the world. Meanwhile, the rise of social media influencers has met with some pushback; from regulation in UAE, to more tragic and threatening responses in Iraq. This report explores these developments, as well as emerging questions about the rise of fake news on social media, and the role that social networks are playing in Yemen’s civil war.Using a wide variety of academic, industry and media sources, this White Paper features key insights from social media’s development over the previous year.
Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

Increasing photovoltaic (PV) generation in the world’s power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) SkyImager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance forecasts. The SkyImager utilizes a single board computer and high-resolution camera with a fisheye lens housed in an all-weather enclosure. General Purpose IO pins allow external sensors to be connected, a unique aspect is the use of only open source software. Code for the SkyImager is written in Python and calls libraries such as OpenCV, Scikit-Learn, SQLite, and Mosquito. The SkyImager was first deployed in 2015 at the National Renewable Energy Laboratory (NREL) as part of the DOE INTEGRATE project. This effort aggregated renewable resources and loads into microgrids which were then controlled by an Energy Management System using the OpenFMB Reference Architecture. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. As part of a collaborative effort between CPS Energy, UT San Antonio, ENDESA, and Universidad de La Laguna, two SkyImagers have also been deployed in the Canary Islands that utilize stereoscopic images to determine cloud heights. Deployments at three geographically diverse locations not only provided large amounts of image data, but also operational experience under very different climatic conditions. This resulted in improvements/additions to the original design: weatherproofing techniques, environmental sensors, maintenance schedules, optimal deployment locations, OpenFMB protocols, and offloading data to the cloud. Originally, optical flow followed by ray-tracing was used to predict cumulus cloud shadows. The latter problem is ill-posed and was replaced by a machine learning strategy with impressive results. R2 values for the multi-layer perceptron of 0.95 for 5 moderately cloudy days and 1.00 for 5 clear sky days validate using images to determine irradiance. The SkyImager in a distributed environment with cloud-computing will be an integral part of the command and control for today’s SmartGrid and Internet of Things.
Superpixel Segmentation by Object-Based Iterative Spanning Forest

Superpixel segmentation methods aim at representing image objects by the union of connected regions (superpixels). Such aim can be better approximated with a higher number of superpixels per object, which often leads to an unnecessary over-segmentation due to the absence of prior object information. In this work, we extend the Iterative Spanning Forest (ISF) framework to include object information and present a superpixel segmentation method based on object saliency detection. As ISF, the new framework, named Object-based ISF (OISF), relies on multiple executions of the Image Foresting Transform (IFT) algorithm for improved seed sets, such that each seed defines one connected superpixel as a spanning tree rooted at that seed. We describe an IFT-based method for object saliency detection and show that the corresponding saliency maps can improve seed estimation and connectivity function, increasing the superpixel resolution inside a given object. Experimental results on two medical image datasets demonstrate that the proposed OISF-based method outperforms the state-of-the-art in boundary adherence with higher number of superpixels inside the object.
Experimental Verification of Effectiveness of English Language Teaching Using MyEnglishLab

The paper deals with a research focused on the blended learning model represented by an online component MyEnglishLab complementing English paper-based materials. At first, we viewed the blended learning model and we presented it in the context of sustainability in education. This is followed by the description of MyEnglisLab component representing the distant part of blended learning. Then, we presented a complete research carried out within undergraduate students at the Faculty of Science, University of Hradec Kralove attending English courses, where we applied the mentioned teaching design and verifying the effectiveness of MyEnglishLab tool. The study compared two groups of students; the group using MyEnglishLab reached better results.
Tightening Monogamy and Polygamy Inequalities of Multiqubit Entanglement

Monogamy and polygamy relations of quantum entanglement characterize the sharing and distribution of entanglement in a multipartite system. Multiqubit entanglement can be characterized entirely with bipartite combinations by saturating the monogamy and polygamy inequalities. In this paper, we tighten monogamy and polygamy constraints for the squared convex-roof extended negativity and its dual measure by employing a genetic algorithm. This evolutionary algorithm optimizes inequality residual functions to improve the monogamy and polygamy relations of these entanglement measures.
Optimal Path Maps on the GPU

We introduce a new method for computing optimal path maps on the GPU using OpenGL shaders. Our method explores GPU rasterization as a way to propagate optimal costs on a polygonal 2D environment, producing optimal path maps which can efficiently be queried at run-time. Our method is implemented entirely with GPU shaders, does not require pre-computation, addresses optimal path maps with multiple points and line segments as sources, and introduces a new optimal path map concept not addressed before: maps with weights at vertices representing possible changes in traversal speed. The produced maps offer new capabilities not explored by previous navigation representations and at the same time address paths with global optimality, a characteristic which has been mostly neglected in animated virtual environments. The proposed path maps partition the input environment into the regions sharing a same parent point along the shortest path to the closest source, taking into account possible speed changes at vertices. The proposed approach is particularly suitable for the animation of multiple agents moving toward the entrances or exits of a virtual environment, a situation which is efficiently represented with the proposed path maps.
Smart Camera Aware Crowd Counting via Multiple Task Fractional Stride Deep Learning

Estimating the number of people in highly clustered crowd scenes is an extremely challenging task on account of serious occlusion and non-uniformity distribution in one crowd image. Traditional works on crowd counting take advantage of different CNN like networks to regress crowd density map, and further predict the count. In contrast, we investigate a simple but valid deep learning model that concentrates on accurately predicting the density map and simultaneously training a density level classifier to relax parameters of the network to prevent dangerous stampede with a smart camera. First, a combination of atrous and fractional stride convolutional neural network (CAFN) is proposed to deliver larger receptive fields and reduce the loss of details during down-sampling by using dilated kernels. Second, the expanded architecture is offered to not only precisely regress the density map, but also classify the density level of the crowd in the meantime (MTCAFN, multiple tasks CAFN for both regression and classification). Third, experimental results demonstrated on four datasets (Shanghai Tech A (MAE = 88.1) and B (MAE = 18.8), WorldExpo'10(average MAE = 8.2), NS UCF_CC_50(MAE = 303.2) prove our proposed method can deliver effective performance.
Adaptive Makeup Transfer via Bat Algorithm

With the advent of the artificial intelligence (AI) era, the beauty camera is widely used, and makeup transfer has attracted increasing attention. In this paper, we propose an adaptive makeup transfer based on the bat algorithm to solve the problem that only a single makeup effect can be transferred. According to the characteristics of makeup style, the algorithm optimizes the weight value to get the appropriate makeup lightness by using the adaptive method. The improved algorithm can not only help to get the optimal weight values in the process of transferring the same makeup style to different targets, but also to transfer different makeup styles to the same target. Moreover, this algorithm can choose the most suitable makeup style and also the most appropriate lightness for a certain person. Experimental results show that the algorithm proposed in the paper has a better effect than the existing algorithm of makeup transfer, and the algorithm can provide users with a suitable makeup style and appropriate lightness.
Deep compact discriminative representation for unconstrained face recognition

Convolutional Neural Network has been widely used in pattern recognition community, especially face recognition. Loss function, as a supervisory signal to learn a CNN model, plays an important role in obtaining the desired facial features. However, how to design a loss function to make the features more compact and discriminative for unconstrained face recognition, is still an open problem. In this paper, we propose two novel loss functions, Compact Discriminative loss and Advanced Compact Discriminative loss. They supervise CNN to map the raw data onto the face feature space, where the intra-class space is compact and inter-class spaces have sensible gaps, by constraining the intra-class variations and the inter-class variations simultaneously. Three CNNs (i.e. LeNet, CNN-M and ResNet-50) are used to analyze the effectiveness of the proposed approaches, the obtained models are evaluated on several famous benchmark databases, such as MNIST, LFW, FGLFW, YTF and IJB-A. Experimental results show that the proposed losses are effective for face recognition, and can easily generate comparable results than related state-of-the-art methods.
Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method

Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named "supervised kernel locality-based discriminant neighborhood embedding," which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only nonlinear and complex variation of face images is effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, in order to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. Experiment results reveal that our method consistently outperforms its competitors, which demonstrates strong potential to be implemented in many real-world systems.
Principles of Green Data Mining

This paper develops a set of principles for green data mining, related to the key stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment.The principles are grounded in a review of the Cross Industry Standard Process for Data mining (CRISP-DM) model and relevant literature on data mining methods and Green IT.We describe how data scientists can contribute to designing environmentally friendly data mining processes, for instance, by using green energy, choosing between make-or-buy, exploiting approaches to data reduction based on business understanding or pure statistics, or choosing energy friendly models.We derived our principles by analyzing the CRISP-DM data mining process and literature on green IT and data mining.In a first step, we identified factors determining energy consumption.In a second step, we identified individual steps of the CRISP-DM process by investigating possibilities for reduction of each factor.We limited our analysis to those aspects that can be directly influenced by data scientists, including the choice of data, its representation, as well as processes and techniques used throughout the data
An Introduction to RA21: Taking Authentication Beyond IP Addresses

Providing access to electronic resources is a core service for most libraries, and for more than two decades librarians have used Internet Protocol (IP) addresses as a way to authenticate users and prove they should have access to their institution’s licensed materials. But in recent years, IP addresses have become a less accurate method of determining whether a user is affiliated with a particular library. Key players in the publishing industry and academia are working together on a new set of protocols to replace IP authentication called Resource Access for the 21st Century, or RA21. This column will briefly explore what RA21 is, what problems it purports to solve, and what problems it may create. A list of resources for further reading on RA21 is provided.
Lexicon-based emotion analysis in Turkish

In this paper, we proposed a lexicon for emotion analysis in Turkish for six emotional categories happiness, fear, anger, sadness, disgust, and surprise. Besides, we also investigated the effects of a lemmatizer and a stemmer, two term-weighting schemes, four lexicon enrichment methods, and a term selection approach for lexicon construction. To do this, we generated Turkish emotion lexicon based on a dataset, TREMO, containing 25,989 documents. We then preprocessed the documents to obtain dictionary and stem forms of each term using a lemmatizer and a stemmer. Afterwards, we proposed two different weighting schemes where term frequency, term-class frequency and mutual information (MI) values for six emotion categories are taken into consideration. We then enriched the lexicon by using bigram and concept hierarchy methods, and performed term selection for efficiency issues. Then, we compared the performance of lexicon-based approach with machine learning based approach by using our proposed lexicon. The experiments showed that the use of the proposed lexicon efficiently produces comparable results in emotion analysis in Turkish text.
Trends in Detection and Characterization of Propaganda Bots

Since the revelations of interference in the 2016 US Presidential elections, the UK's Brexit referendum, the Catalan independence vote in 2017 and numerous other major political discussions by malicious online actors and propaganda bots, there has been increasing interest in understanding how to detect and characterize such threats.We focus on some of the recent research in algorithms for detection of propaganda botnets and metrics by which their impact can be measured.
The advantages and challenges of “big data”: Insights from the 14 billion word iWeb corpus

The iWeb corpus contains nearly 14 billion words from 22 million web pages, and it has been designed in a way that allows users to quickly and easily create “Virtual Corpora”, in order to focus on websites that are related to their areas of interest. The data from this very large corpus provides very detailed information on syntactic, morphological, lexical, and semantic phenomena, in ways that would never be possible with a small 100 million or 500 million word corpus. In addition, the corpus provides a number of features that are not available with other large corpora, such as the ability to perform advanced searches of the top 60,000 words in the corpus, and to see a wealth of information on each of these words - definitions, links to images and audio, translations, detailed frequency information, related topics, collocates, word clusters, re-sortable concordance lines, and much more. Finally, we discuss the challenges of large corpora, and how the corpus architecture that is used for iWeb has uniquely been designed to address these challenges. (Brigham Young University · Kyung Hee University)
Deep Image Compression in the Wavelet Transform Domain Based on High Frequency Sub-Band Prediction

In this paper, we propose to use deep neural networks for image compression in the wavelet transform domain. When the input image is transformed from the spatial pixel domain to the wavelet transform domain, one low-frequency sub-band (LF sub-band) and three high-frequency sub-bands (HF sub-bands) are generated. Low-frequency sub-band is firstly used to predict each high-frequency sub-band to eliminate redundancy between the sub-bands, after which the sub-bands are fed into different auto-encoders to do the encoding. In order to further improve the compression efficiency, we use a conditional probability model to estimate the context-dependent prior probability of the encoded codes, which can be used for entropy coding. The entire training process is unsupervised, and the auto-encoders and the conditional probability model are trained jointly. The experimental results show that the proposed approach outperforms JPEG, JPEG2000, BPG, and some mainstream neural network-based image compression. Furthermore, it produces better visual quality with clearer details and textures because more high-frequency coefficients can be reserved, thanks to the high-frequency prediction.
Analysis and Detection of Content based Video Retrieval

Based Video Retrieval (CBVR) System has been investigated over past decade it's rooted in many applications like developments and technologies.The demand for extraction of high level semantics contents as well as handling of low level contents in video retrieval systems are still in need.Hence it motivates and encourages many researchers to discover their knowledge across CBVR domain and contribute their work to make the system more effective and useful in developing the system application.This paper highlights comprehensive and extensive review of CBVR techniques for detection of region of interest in a given video.The experiment is carried out for the detection of ROI using ACF detector.The detection rate of ROI is observed competitive and satisfactory.