Reviewerly/exHarmony huggingface dataset:
Looking for Efficient Methods to Collect and Geolocalise Tweets

In the era of Big Data, Internet became one of the main data sources: data can be collected for relatively low costs and can be used for a wide range of purposes. The target of this paper is to compare three alternative methods of Twitter posts collection. We evaluate three main criteria: the accuracy, the relative reliability and the relative efficiency of each method, using first NUTS level of Great Britain data. One of the three methods stands as being the best (reusable) compromise.
AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY RARE ITEMSETS OVER UNCERTAIN DATABASES

In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention.Association Rule Mining (ARM) is a well known and most popular technique of Data Mining.It identifies itemsets from the dataset which appears frequently and generates association rules.This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets.In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue.Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining.But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain.High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold.Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit.Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest.In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases.This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al.The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.
Eye Control System Development and Research of Effects of Color of Icons on Visual Search Performance Based on the System

Traditional interactive approach which has been developed very mature has evolved from mouse control to many other forms. Eye controlled, as one of new interaction form, will help people with physical disability to manipulate devices as normal users. Meanwhile, because of its natural and high-efficiency, eye controlled system could be the future trend not only for the disabled but also ordinary people. This study proposed an eye controlled system frame to help researchers and designers to explore eye control system interface design because eyes are the only real-time input medium for eye control system. This study firstly analyzes and summarize the difference of eye-control system and traditional interface through surveys and literature reviews. Then according to the uniqueness of eye-controlled interaction, an eye control interactive logic algorithm is developed through the Tobii API combined with the Unity API. Experiments investigated the effects of color combination of icon on visual search performance were done based on the proposed system.
Neural networks and Keras

This chapter discusses the basic operation of an artificial neural network which is the major paradigm of deep learning. The name derives from an analogy to a biological brain. The discussion begins by outlining the basic operations of neurons in the brain and how these operations are abstracted by simple neuron models. It then builds networks of artificial neurons that constitute much of the recent success of AI. The focus of this chapter is on using such techniques, with subsequent consideration of their theoretical embedding.
Multi-Device Complementary View Adaptation with Liquid Media Queries

The design of responsive Web applications is traditionally based on the assumption that they run on a single client at a time. Thanks to CSS3 media queries, developers can declaratively specify how the Web application UI adapts to the capabilities of specific devices. As users own more and more devices and they attempt to use them to run Web applications in parallel, we propose to extend CSS media queries so that they can be used to adapt the UI of liquid Web applications while they are dynamically deployed across multiple devices.In this paper we present our extension of CSS media queries with liquid-related types and features, allowing to detect the number of devices connected, the number of users running the application, or the role played by each device. The liquid media query types and features defined in this paper are designed and suitable for liquid component-based Web architectures, and they enable developers to control the deployment of individual Web components across multiple browsers. Furthermore we show the design of liquid media queries in the Liquid.js for Polymer framework and propose our adaptation algorithms. We describe multiple adaptation policies and discuss the implications of the multi-device adaptation from the perspective of the developers and users of a Web application. Finally we showcase the expressiveness of the liquid media queries to support real-world examples and evaluate the algorithmic complexity of our approach.
The Effects of College Students` Career Stress on Leisure Function, Constraint, and Quality of Life

The purpose of this research was to investigate effects of college students" career stress on leisure function, constraint, and quality of life. In order to achieve its goals, this research has established hypothesis with theoretical background and analysis with actual examination. The examination for this research was carried out in between 2nd May, 2019 to 5th May, 2019. Among a total of 400 questionnaires distributed, 336 questionnaires been used for analysis. Results of this study are as follows: Firstly, career stress had a negative effect with leisure function and quality of life while career stress had a positive effect with leisure constraint. Secondly, leisure function had a negative effect with leisure function while leisure function had a positive effect with quality of life. Finally, leisure constraint had showed a negatively relation with quality of life. Thus, the results that university and public attention to the necessity of establishing environmental leisure activity support of college students, which could positively enhance their leisure function.
RSNet: A Compact Relative Squeezing Net for Image Recognition

Convolutional neural networks(CNN) are showing powerful performance on image recognition tasks. However, when CNN is applied to mobile devices, with limited computing and memory resource, it requires more compact design to maintain a relatively high performance. In this paper, we propose Relative Squeezing Net(RSNet) that provides technical insight into CNN structure for designing a compact model. In an endeavor to improve CondenseNet, we introduce Relative-Squeezing bottleneck where output is weighted percentage of input channels. The design of our bottleneck can transmit diverse and most useful features at all stages. We also employ multiple compression layers to constrain the output channels of feature maps which can eliminate superfluous feature maps and transmit powerful representations to next layers. We evaluate our model on two benchmark datasets; CIFAR and ImageNet. Experimental results show that RSNet achieves state-of-the-art results with less parameters and FLOPs and is more efficient than compact architectures such as CondenseNet, MobileNet and ShuffleNet.
Improvement of Big Data Stream Mining Technique for Automatic Bone Age Assessment

Rapid technology growth has stimulated automated systems development in the medical field. Automatic bone age evaluation is an example of the implementation of this technology in the medical field. The automated assessment is done based on the left-hand X-ray images. This assessment helps the radiologist and pediatrician evaluate children's growth. A system that can generate a precise and reliable prediction is essential. Thus the main challenge is to determine the suitable technology that can generate a reliable forecast, mainly when working with a large quantity of data. Big data is a growing trend; practical computing challenges created by data streams can be found in several types of applications. It is known that data streams are usually obtained from sensors and monitors which accumulatively can make data very large in volume. This can result in the inability of real-time processing to be carried out. In this paper, the data stream technique is utilized to assess and predict bone ages. The analysis process is carried out in real-time when the data arrives so that the process of storing new data is done after the data is analyzed. A 9 GB sized-dataset consisting of 12,611 images were used. The images have various resolutions. We extracted and analyzed image features by using Canny Edge feature extraction. To predict bone age from those extracted features, we enhance the data stream mining technique base on tree stream mining. We use MAE or Mean Absolute Error, RMSE or Root Mean Squared Error, and MAPE or Mean Absolute Percentage Error as metrics in this measurement. The outcomes of our experiment show that our approach in data stream mining can increase performance measurements. The MAPE of our approach gives a 7% lower error evaluation compared to the standard method.
Stronger Baseline for Vehicle Re-Identification in the Wild

Recently, re-identification tasks in computer vision field draw attention. Vehicle re-identification can be used to find the suspect car (target) from a vast surveillance video dataset. One of the most critical issues in the vehicle re-identification task is how to learn the effective feature representation. In general, pairwise learning such as the contrastive and triplet loss functions is adopted to learn the discriminative feature based on the convolution neural network. A good backbone network will lead to a significant improvement in the car re-identification task. In this paper, a stronger baseline method is proposed to achieve a better feature representation ability. First, we integrate the shift-invariant convolutional neural network with ResNet backbone to enhance the consistency feature learning. Afterward, a multi-layer feature fusion module is proposed to incorporate the middle- and high-level features to further improve the performance of car re-identification. Experimental results demonstrated that the proposed stronger baseline method achieves state-of-the-art performance in terms of mean averaging precision.
Correction: OpinionML—Opinion Markup Language for Sentiment Representation. Symmetry 2019, 11, 545

The authors wish to make the following corrections to their paper [...]
No-Reference Stereoscopic Image Quality Assessment Based On Shuffle-Convolutional Neural Network

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has been gaining great attention. In this work, to find a better SIQA method conforming to the perceptual characteristics of our brain, we propose a two-channel convolutional neural network (CNN) based on shuffle unit, which is called SCNN, for no-reference SIQA. The shuffle unit is used to mix up the features extracted from the left and right views to complete information communication between the two views. Different from other SIQA methods, the four shuffle units among proposed model achieve the multiple binocular fusions while processing the left and right views. Moreover, the Shuffle v2 block before the global pooling layer further improves the accuracy of SCNN. In addition, it is worth noting that we employ decorrelated batch normalization (DBN) to obtain the better generalization ability. Experimental results demonstrate that the proposed model outperforms the state-of-the-art no-reference SIQA methods.
Extraction method of position and posture information of robot arm picking up target based on RGB-D data

There is a big error in the traditional method to extract the position and attitude information of the robot. In the process of obtaining the target attitude, a method of extracting the target attitude information of robot arm based on RGB-D data is proposed. The position and attitude of the manipulator target are acquired by depth image processing, and the detected target position is sent to the manipulator control node, and the feature points of the manipulator are extracted. The 3-D mapping is carried out on the acquired RGB image, and the depth and RGB values of feature points, as well as position and attitude information are calculated by using the Gauss mixture model. Finally, the target is extracted by combining the covariance matrix of feature points. The experimental results show that the co-ordinate error and angle error of the robot arm extracted by this method are small. The maximum extraction error is only 28%, which is much lower than the traditional method, which shows that the proposed method is more applicable.
Underwater Image Enhancement by Rayleigh Stretching in Time and Frequency Domain

Image restoration and enhancement play an important role in underwater image applications. How to solve the negative gloomy underwater effects resulting from scattering and color distortion has become the key issues. In this study, we proposed a Rayleigh stretching-based image enhancement method. The method includes a few steps which are background color correction, Rayleigh stretching in time domain, discrete wavelet transform with Contrast Limited Adaptive Histogram Equalization (CLAHE), contrast adjustment, and sharpening. Experimental results through both the qualitative and quantitative analyses demonstrate that our approach greatly improves visibility and contrast and obtains best values with respect to entropy and underwater image quality measure (UIQM), respectively.
An Improved QoE Evaluation Model for HTTP Adaptive Streaming

HTTP adaptive streaming (HAS) is an adaptive bitrate streaming technique that enables high quality streaming of media content over the Internet delivered from conventional HTTP web servers. ITU-T Rec. P.1203.3 is the first standardized Quality of Experience model for audiovisual HTTP Adaptive Streaming. It takes into account the subjective impact of HAS-typical effects(such as buffering, quality switches) on users. But the buffering inputs required for the model is too complex and redundant, and it does not consider the impact of the worst video quality on QoE. In the paper we optimize the ITU-T Rec. P.1203.3 model in the above two aspects, simplify model input and improve model accuracy and stability.
Peningkatan Aktifitas Kolaboratif Dan Hasil Belajar. Siswa Melalui Guided Discovery Learning Berbantuan Puzzel

Nowadays, the government is promoting the Individual Learning Activity Sheet (UKBM) for the student in mathematics learning. UKBM provides opportunities for students to study independently and provides the chance for students to complete assignments (UKBM) differently for each student in one class. The aim of this study was to increase collaborative activity skills and student’s learning outcomes through puzzle-assisted Guided Discovery Learning learning models. This study was a classroom. action research within two cycles consisting of the planning stage, implementation stage, observation stage, and reflection stage. The instruments used were observation sheets and test sheets. The teaching material is UKBM. The results of a guided discovery learning study assisted by puzzles with UKBM teaching materials there is an increase in student’s collaborative abilities and learning outcomes. Keywords: Guided Discovery Learning, Collaborative Ability, Learning Outcomes, Media Puzzle
Serverless Mobile Applications Development

Principles of mobile applications development, requiring no server code, are described. Database, user authentication and notification sending functions are suggested to be implemented by BaaS model (Backend as a Service). Example architecture of serverless mobile application using Google Firebase cloud services is shown. Brief description of basic interactions between serverless architecture components is given.
Review in Data Stream Mining in Big Data

Data stream mining in big data is a process in which large streams of real-time data are processed with the sole aim of extracting insights and useful trends out of it.Streaming data study in real time is fetching the efficient and fastest way to obtain constructive knowledge from what is occurrence now, allowing concern to react quickly when problems emerge to detect new trends helping to recover their performance.In this paper, we have a tendency to reward the theoretical foundations of data stream in big data analysis and establish potential directions of future analysis.Mining data stream and big data techniques are being reviewed.
Text independent voiceprint recognition model based on I-vector

Speaker Recognition is a technology for identifying people based on the Voiceprint characteristics contained in voice, and is a kind of biometric authentication technology.According to different recognition methods, Voiceprint recognition can be divided into three categories [1].(1) Speaker discrimination is to distinguish who is talking from a given speaker set; (2) speaker confirmation is to judge whether someone is speaking or not; (3) speaker segmenting and clustering is to distinguish the switching time of different speakers based on the voice formed by multiple speakers.
Political micro-targeting in an era of big data analytics

Recent debates instigated by the Cambridge Analytica/Facebook scandal reveal the transformative effect of big data analytics and artificial intelligence in modern political campaigning. This chapter summarises and contextualises the contributions contained in the present volume, which explore the issues arising from political micro-targeting in the age of big data analytics from an interdisciplinary and international perspective. The profiling practices that underlie data-driven political campaigns raise profound privacy issues. The use of the personal data of voters by political parties and movements, data brokers and the platforms on which they engage with the electorate are in many jurisdictions not adequately regulated. The contributions to this volume demonstrate that enhanced regulation of the use of personal data in the political process is needed not only to safeguard individual privacy but also for the protection of public discourse and democratic participation more broadly. Our democracy will suffer if we do not address the new threats to privacy arising from data-driven political campaigning.
Improving Communication in Risk Management of Health Information Technology Systems by means of Medical Text Simplification

Health Information Technology Systems (HITS) are increasingly used to improve the quality of patient care while reducing costs. These systems have been developed in response to the changing models of care to an ongoing relationship between patient and care team, supported by the use of technology due to the increased instance of chronic disease. However, the use of HITS may increase the risk to patient safety and security. While standards can be used to address and manage these risks, significant communication problems exist between experts working in different departments. These departments operate in silos often leading to communication breakdowns. For example, risk management stakeholders who are not clinicians may struggle to understand, define and manage risks associated with these systems when talking to medical professionals as they do not understand medical terminology or the associated care processes. In order to overcome this communication problem, we propose the use of the “Three Amigos” approach together with the use of the SIMPLE tool that has been developed to assist patients in understanding medical terms. This paper examines how the “Three Amigos” approach and the SIMPLE tool can be used to improve estimation of severity of risk by non-clinical risk management stakeholders and provides a practical example of their use in a ten step risk management process.
Efficient Side-Channel Secure Message Authentication with Better Bounds

We investigate constructing message authentication schemes from symmetric cryptographic primitives, with the goal of achieving security when most intermediate values during tag computation and verification are leaked (i.e., mode-level leakage-resilience). Existing efficient proposals typically follow the plain Hash-then-MAC paradigm T = TGenK(H(M)). When the domain of the MAC function TGenK is {0, 1}128, e.g., when instantiated with the AES, forgery is possible within time 264 and data complexity 1. To dismiss such cheap attacks, we propose two modes: LRW1-based Hash-then-MAC (LRWHM) that is built upon the LRW1 tweakable blockcipher of Liskov, Rivest, and Wagner, and Rekeying Hash-then-MAC (RHM) that employs internal rekeying. Built upon secure AES implementations, LRWHM is provably secure up to (beyond-birthday) 278.3 time complexity, while RHM is provably secure up to 2121 time. Thus in practice, their main security threat is expected to be side-channel key recovery attacks against the AES implementations. Finally, we benchmark the performance of instances of our modes based on the AES and SHA3 and confirm their efficiency.
MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture

Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results: - any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs; - assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - η fraction of machines (for an arbitrarily small constant η) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup. As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques.