Reviewerly/exHarmony huggingface dataset:
Foreground Extraction Combining Graph Cut and Histogram Shape Analysis

This paper presents a foreground extraction model which develops the Grab Cut model by applying the histogram shape analysis method. In this model, the foreground extraction is formulated as an inference problem based on edges and appearance models. The inference is solved via a minimum cut/maximum flow algorithm scheme, which allows incorporation of edge information and automatic tuning of parameters in appearance models. We use the histogram shape analysis method to analyze the intensity distribution of an image, and estimate the optimal number of regions in order to best model appearances of the foreground and background. The appearance models are defined as a maximum likelihood form instead of the original Gaussian Mixture Models in order to distinguish the small regions in an image. Numerical experimental results on the Berkeley segmentation database and Weizmann horse's database indicate that, compared to existing foreground extraction models, the proposed model provides comparable performance in terms of segmentation metric and computational cost, while being insensitive to the small region in an image.
Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network

The accuracy of tourist flow prediction is crucial to the sustainable development of tourism industry.However, it is very difficult to forecast the highly nonlinear tourist flow in an accurate manner.The artificial neural network (ANN) has been widely adopted to predict nonlinear time series, but its shallow structure cannot effectively learn the features of high-dimensional tourist flow data.To solve the problem, this paper puts forward a tourist flow prediction model based on deep learning (DL).First, the deep belief network (DBN), with its strong ability to extract nonlinear features, was employed to extract effective features through unsupervised learning of historical tourist flow.Next, the echo state network (ESN) was effectively fused with the DBN.The ESN was placed at the top layer of the tourist flow prediction model, serving as the logic regression layer.Finally, the offline training and prediction effects of the proposed ESN-DBN were verified through experiments on the holiday tourist flow data extracted from a tourist center, and compared with those of two classical prediction models, namely, backpropagation neural network (BPNN) and autoregressive integrated moving average (ARIMA).The results show that the ESN-DBN achieved a mean absolute percentage error was below 12% and a rational computing time; the proposed model also outperformed the two classical models in prediction accuracy.The research results provide an important reference for the forecast of tourist flow and planning of tourism development.
Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts

Combining first order logic rules with a Knowledge Graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain confidences, achieve state-of-the-art performance. However, the existing methods either cannot support the transitivity and composition rules or take soft rules as regularization terms to constrain derived facts, which is incapable of encoding the logical background knowledge about facts contained in soft rules. In addition, previous works performed one time logical inference over rules to generate valid groundings for modeling rules, ignoring forward chaining inference, which can further generate more valid groundings to better model rules. To these ends, this paper proposes Soft Logical rules enhanced Embedding (SoLE), a novel KG embedding model equipped with a joint training algorithm over soft rules and KG facts to inject the logical background knowledge of rules into embeddings, as well as forward chaining inference over rules. Evaluations on Freebase and DBpedia show that SoLE not only achieves improvements of 11.6%/5.9% in Mean Reciprocal Rank (MRR) and 18.4%/15.9% in HITS@1 compared to the model on which SoLE is based, but also significantly and consistently outperforms the state-of-the-art baselines in the link prediction task.
Information Losses in Neural Classifiers From Sampling

This article considers the subject of information losses arising from the finite data sets used in the training of neural classifiers. It proves a relationship between such losses as the product of the expected total variation of the estimated neural model with the information about the feature space contained in the hidden representation of that model. It then bounds this expected total variation as a function of the size of randomly sampled data sets in a fairly general setting, and without bringing in any additional dependence on model complexity. It ultimately obtains bounds on information losses that are less sensitive to input compression and in general much smaller than existing bounds. This article then uses these bounds to explain some recent experimental findings of information compression in neural networks that cannot be explained by previous work. Finally, this article shows that not only are these bounds much smaller than existing ones, but they also correspond well with experiments.
Providing enhanced security in IoT based smart weather system

Weather reporting system consist multiple devices which are playing dynamic role in producing dynamic environmental parameters such as temperature, humidity, and air pollution. It was a tedious task to bring all the devices together and make them interleaving to produce relevant measurement. Recent trends have proven that the IoT has brought all the devices and peripherals in one place to make more flexible and smart measurement. In the traditional weather monitoring system measurement method is not real time, not continuous and also tedious job to take continuous measurement. But, the IoT has completely changed the measurement scenario and improved the consistency of the measurement. In the present work the environmental parameters such rainfall, temperature, humidity, and density of carbon dioxide in the air are measured with sensors. The Arduino Uno card gathers all information from the devices which are associated with its port pins. The information is sent to cloud server for record and future retrieving. At the same time the data security in cloud been assured with encryption and decryption data while retrieving the information from cloud. This enhances the security, ease of accessing the cloud data from mobile applications, provides wise predictions and minimizes the communication overhead.
Using AR Interfaces to Support Industrial Maintenance Procedures

Industries are becoming more and more digitized to better implement intelligent and predictive maintenance support systems, aligned with Industry 4.0, which requires the progressive digitization of data collection and processes. Maintenance interventions, in an evolving technological context, are increasingly more complex and difficult for technicians to perform. In these environments, the use of Augmented Reality (AR) to help assist and guide in the maintenance operations, can accomplish a considerable gain in productivity. AR allows to superimpose information objects in real scenes, such as text, images, audiovisuals, and 2D/3D model animations, making available contextual information about the process, based on location and perspective. This paper describes the design and implementation of a prototype augmented reality application to support maintenance tasks inside a metal stamping production unit, that produces components for the automotive sector. It aims to train and guide personnel during the maintenance operations, and offering an extra channel to reach expert help.
Quantum Beat between Sunlight and Single Photons

We demonstrate fourth-order quantum beat between sunlight and single photons from a quantum dot. With a fast time-resolved detection system, we observed high-visibility quantum beat between the independent photons of different frequencies from the two astronomically separated light sources. The temporal dynamics of the beat oscillation indicate the coherent behavior of the interfering photons, and the raw visibility of two-photon interference shows violation of the classical limit with a frequency mismatch of three-times the line width.
Correction to Removal of Cu(II) and Fulvic Acid by Graphene Oxide Nanosheets Decorated with Fe<sub>3</sub>O<sub>4</sub> Nanoparticles

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to Removal of Cu(II) and Fulvic Acid by Graphene Oxide Nanosheets Decorated with Fe3O4 NanoparticlesJie LiJie LiMore by Jie Li, Shouwei ZhangShouwei ZhangMore by Shouwei Zhanghttp://orcid.org/0000-0002-4344-6008, Changlun Chen*Changlun ChenMore by Changlun Chenhttp://orcid.org/0000-0003-1636-2412, Guixia ZhaoGuixia ZhaoMore by Guixia Zhaohttp://orcid.org/0000-0003-4537-3508, Xin YangXin YangMore by Xin Yang, Jiaxing LiJiaxing LiMore by Jiaxing Lihttp://orcid.org/0000-0002-7683-2482, and Xiangke WangXiangke WangMore by Xiangke WangCite this: ACS Appl. Mater. Interfaces 2019, 11, 51, 48624Publication Date (Web):December 13, 2019Publication History Published online13 December 2019Published inissue 26 December 2019https://doi.org/10.1021/acsami.9b18912Copyright © 2019 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views1233Altmetric-Citations4LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (742 KB) Get e-AlertsSupporting Info (1)»Supporting Information Supporting Information Get e-Alerts
BLAN: Bi-directional ladder attentive network for facial attribute prediction

Deep facial attribute prediction has received considerable attention with a wide range of real-world applications in the past few years. Existing works almost extract abstract global features at high levels of deep neural networks to make predictions. However, local features at low levels, which contain detailed local attribute information, are not well exploited. In this paper, we propose a novel Bi-directional Ladder Attentive Network (BLAN) to learn hierarchical representations, covering the correlations between feature hierarchies and attribute characteristics. BLAN adopts layer-wise bi-directional connections based on the autoencoder framework from low to high levels. In this way, hierarchical features with local and global attribute characteristics could be correspondingly interweaved at each level via multiple designed Residual Dual Attention Modules (RDAMs). Besides, we derive a Local Mutual Information Maximization (LMIM) loss to further incorporate the locality of facial attributes to high-level representations at each hierarchy. Multiple attribute classifiers receive hierarchical representations to produce local and global decisions, followed by a proposed adaptive score fusion module to merge these decisions for yielding the final prediction result. Extensive experiments on two facial attribute datasets, CelebA and LFWA, demonstrate that our BLAN outperforms state-of-the-art methods.
Crowd Escape Event Detection via Pooling Features of Optical Flow for Intelligent Video Surveillance Systems

In this paper we propose a method for automatic detection of crowd escape behaviour.Motion features are extracted by optical flow using Lucas-Kanade derivative of Gaussian method (LKDoG) followed by robust probabilistic weighted feature pooling operation.Probabilistic feature polling chooses the most descriptive features in the sub-block and summarizes the joint representation of the selected features by Probabilistic Weighted Optical Flow Magnitude Histogram (PWOFMH) and Probabilistic Weighted Optical Flow Direction Histogram (PWOFDH).One class Extreme Learning Machine (OC-ELM) is used to train and test our proposed algorithm.The accuracy of our proposed method is evaluated on UMN, PETS 2009 and AVANUE datasets and correlations with the best in class techniques approves the upsides of our proposed method.
Risk Assessment and Chemical Fractionation of Heavy Metals by BCR Sequential Extraction in Soil of the Sapanca Lake Basin, Turkey

Sapanca Lake is an important drinking water source located by D-100 highway in the north and E-80 (TEM Anatolian Highway) and a railway line in the south.Heavy metal concentrations in roadside soils result from vehicle exhausts and the corroding metal parts of vehicles.Due to the difficulty of removing heavy metals from the soil, a significant pollution problem arises and this pollution also affects the water resources by means of rain.Although there are several industries, the highway located near the lake is the most important pollutant source for Sapanca Basin.Therefore, this study evaluated heavy metal concentrations, the chemical fractions of the metals and ecological risks (by using C f , RAC and PERI) in the soil samples collected seasonally between 2015 and 2017 in Sapanca Lake Basin.Al and Fe were determined at very high amounts in all stations and the relationship between mean concentrations of other metals was determined to be Zn>Ba>As>Ni>Cr>Pb>Cu>Co>Cd.However, Zn, Ba, As, Ni, Cr, Pb, Cu, Co and Cd mean concentrations were determined as 87.63 mg/kg, Ba 86.87 mg/kg, 80.40 mg/kg, 58.62 mg/kg, 50.42 mg/kg, 41.90 mg/kg, 38.16 mg/kg, 13.98 mg/kg, 2.89 mg/kg respectively.Al, Fe, Co, Cr, Cu, Ni and Zn are mainly found in residual fractions in soil.According to the environmental risk assessment on the basis of soil stations, the 7 th station has the highest GC f value, which means that the soil sample presents the highest environmental risk according to the contamination factors.On the other hand, Cd has serious potential ecological risk and As has considerable potential ecological risk in all stations.
A study on quality of work life amongst employees working in the Kingdom of Saudi Arabia

It is a well-known fact that quality of work life (QWL) is considered as a philosophy to make the people more effective at workplace by understanding their psychological well-being and involvement or attachment.The main objective of the current investigation was to understand the degree of the QWL amongst the employees working in the Saudi Arabia.The random sampling method was applied to select the data from employees working in the public and private sectors.The QWL scale and biographical information sheet were used to gather the information from the employees.Based on the research objective and design of the investigation, factorial analysis, Cronbach's alpha value for reliability check and other statistical techniques were used to explore the facts of the data.The results show that six factors such as autonomy, inter-group relation, recognition, economic benefits, self-respect and supervisory relations were most important determinants of QWL; married and public sector employees found to have high mean score on QWL as compared with their counterparts and majority of employees in the company were having low level of quality of work life.Certain mechanism was discussed to enhance the QWL of the employees to make the organization more effective and viable.
Semantic Feature Analysis Model: Linguistics Approach in Foreign Language Learning Material Developmen

The purpose of this study was to investigate the effect of foreign language learning material development based on the semantic feature analysis model in improving student's learning achievement in English writing and translation subject.This study used classroom action research approach developed by Elliot.Data was collected by using translation test.The total of participants was 45 students who were students Department of English Literature.The data analysis technique used consisted of descriptive statistics and paired sample t-test.This model was implemented during 6 meetings.The result showed that there was a significant increase on the student's learning achievement both in writing class and translation class.It was proved through paired sample t test analysis result which showed that sig.value (0.00) is lower than 0.05 (significance standard).It proved that SFA model can be used as one of effective models in foreign language learning and writing.
VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization

This paper describes our submission to the TL;DR challenge.Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms.However, these models suffer from their extractive nature as they learn to copy words from the source text.In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue.We also propose a Unified Summarization Framework for the generation of summaries.Our model eliminates non-critical information at a sentencelevel with an extractive summarization module and generates the summary word by word using an abstractive summarization module.To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model.We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries.
Environmental Monitoring System Based on Blockchain

With the application of information technology, biotechnology, remote sensing technology, etc, great breakthroughs have been made in the field of environmental protection. While there are still problems within existing environmental protection system, such as the hardware fraud and software fraud of environmental data. In this paper, an environmental monitoring system based on blockchain is proposed, within which the environmental data of the whole network is unified and data processing, consensus and pubilcation requrie multiparty anthentication. As a result, falsification of environmental data will be prevented by adopting the proposed system. In addition, three-dimensional enviromental monitoring architecture and intelligent trusted device are propoed, which can ensure the integrety and originity of the environmental data collected by the devices.
Financial sector development convergence in Africa: Evidence from bank- and market-based measures

In this paper, we employ data from 46 African countries over the period 1980–2014 to examine financial sector development convergence, using bank- and market-based measures of financial development. Within the framework of the generalized method of moments (GMM), we present evidence that both the bank– and market–based financial sector development in Africa diverge over time. However, we find strong evidence of financial development divergence when using bank-based financial sector development indicators whereas this evidence is weaker for market-based indicators. Given the divergence in the level of finance, the gap between countries with underdeveloped and well–developed financial markets will continue to widen as financially less developed countries do not appear to catch-up with the financially more developed economies. Keywords: Financial development; divergence, convergence, AfricaJEL Classification: F15, F36, G01, O55
Klasifikasi Berita Hoax Dengan Menggunakan Metode Naive Bayes

&lt;p&gt;&lt;em&gt;Hoaxes contain false news or non-sourced news. Today, hoaxes are very widely spread through internet media. The development of information technology that has so quickly triggered the spread of hoax information through the internet has become uncontrolled. So we need an intelligent system that can classify hoax news content that is spread through internet media. The hoax classification process can be done through the preprocessing stage then weighting the word and classification using naive bayes. Measurements were made using the 10-ford cross validation method. The results obtained from these measurements, it is known that the value of fold 6 has the highest accuracy, which is equal to 85.28% which is classified as relevant documents as much as 307 and irrelevant as much as 53 or an error rate of 14.72%. While the average value based on hoax news and true news value precision 0.896 and recall 0.853&lt;/em&gt;&lt;/p&gt;
Prox-SGD: Training Structured Neural Networks under Regularization and Constraints

In this paper, we consider the problem of training neural networks (NN). To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint). This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm. We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence. We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point. Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.