Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Developed Empirical Models for Predicting Global Solar Radiation in Western Region of India

For the prediction of solar radiation, various models have been developed in literature with sunshine hours as prime parameter. However, an extensive amount of work is still required correlating the sunshine hours and other parameters. Seven models each for predicting mean global radiation and mean diffuse radiation has been derived in this research work for city Amravati (20.9374 o N, 77.7796 o E), correlating with sunshine hours, average temperature and relative humidity. Relative humidity and temperature together with sunshine can prove to be a best alternative for predicting solar radiation at places where direct measurement is unavailable. Both linear and non-linear correlations developed are compared with conventional models and measured values available for last five years from the meteorological data. The validation is done using several statistical parameters viz. mean absolute percentage error (MAPE), mean bias error (MBE), root mean square error (RMSE) and R 2 . TheÂ  performance evaluation of these models gives the best suitable model for both global and diffuse solar radiation for the city Amravati, Maharashtra.
Energy Attentive Pre-fault Detection Mechanism with Multilevel Transmission for Distributed Wireless Sensor Network

To recognize the vision of ambient intelligence, wireless sensor networks (WSNs) are broadly used to invent real time and smart applications.Energy has been a major concern in sensor networks.Another challenging issue of wireless sensor network is to discover the imperfect node and recover them to maintain network topology.Enhancement of network lifetime, reliability and scalability also depends on fault detection and recovery mechanism.Distributed Energy Attentive Pre Fault Detection Mechanism is proposed in this work for pre fault detection and multipath routing.The novel idea behind this approach is to recognize the sensor fault in advanced and performed the improvement action based on notification parameter like acknowledgement not received, residual energy is below threshold value and hope count distance.It enables a predesor node to rapidly identify and efficiently categories the next energy aware shortest path, upon steering failure, rather than relying from the starting place.Extensive simulations are performed and result demonstrates that the proposed algorithms perform well on various performance indicator metrics such as throughput, energy consumption, packet drop ratio, and communication delay.
Temporal and Spatial Variation Characteristics Analysis of Precipitation Days and Intensity over the Wujiang River Basin in Flood Season

Wang, J.; Peng, T., and Shen, T., 2019. Temporal and spatial variation characteristics analysis of precipitation days and intensity over the Wujiang River Basin in flood season. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 209–219. Coconut Creek (Florida), ISSN 0749-0208.Based on the observation data of 41 surface meteorological stations in Wujiang River Basin from 1960 to 2016, the temporal and spatial variation laws of precipitation days and intensity in flood season (May to September) in Wujiang River Basin are analyzed. It is found that the distribution of precipitation in Wujiang River Basin is characterized by inhomogeneity of spatial change and tendency of temporal change. In terms of spatial distribution, affected by geographical position and topography, Wujiangdu is regarded as the boundary in terms of latitude. From the topographic point of view, because of the terrain in the vicinity of high mountains, precipitation frequency and value are larger, and precipitation also has a good correlation with the spatial distribution of temperature, humidity and wind speed; in the time variation, the frequency of rain days has an upward trend while the corresponding average precipitation is declining. The proportion of heavy rain days is decreasing while the corresponding average precipitation is increasing, and the relative humidity is decreasing. The surface air temperature is on the rise while the wind speed is on a fast downward trend.
Comparison of texture retrieval techniques using deep convolutional features

Considering the complexity of a multimedia society and the subjective task of describing images with words, a visual search application is a valuable tool. This work implements a Content-Based Image Retrieval (CBIR) application for texture images with the goal of comparing three deep convolutional neural networks (VGG-16, ResNet-50, and DenseNet-161), used as image descriptors by extracting global features from images. For measuring similarity among images and ranking them, we employed cosine similarity, Manhattan distance, Bray-Curtis dissimilarity, and Canberra distance. We confirm that global average pooling applied to convolutional layers provides good texture descriptors, and propose to use it when extracting features from VGGbased models. Our best result uses the average pooling layer from DenseNet-161 as a 2208-dim feature vector along with Bray-Curtis dissimilarity. We achieved 73:09% mAP@1 and 76:98% mAP@5 on the Describable Textures Dataset (DTD) benchmark, adapted for image retrieval. Our mAP@1 result is comparable to the state-of-the-art classification accuracy (73:8%). We also investigate the impact on retrieval performance when reducing the number of feature components with PCA. We are able to compress a 2208-dim descriptor down to 128 components with a moderate 3.3 percentage points drop in mAP@1.
The Effect of Beach Sports Power Training on Ankle Joint Injury

Luan, Z., 2019. The effect of beach sports power training on ankle joint injury. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 530–535. Coconut Creek (Florida), ISSN 0749-0208.The power training of beach sports has gradually become a new method of increasing the muscle strength and improving the athletic performance. Anklebone ligamentous injury accounts for a large proportion in the athletic injury. Therefore, in order to improve the quality of beach sports power training, it is necessary to establish the relationship model of ankle joint injury caused by the power training of beach sports, and to study the effect of beach sports power training on ankle joint injury. However, when the current model is used to analyze the impact of beach sports power training on the injury of ankle joint, the principal component of effect of beach sports power training on ankle joint injury cannot be given, and the analysis precision is low. To this end, we proposed a model to evaluate the effect of beach sports power training on ankle joint injury. This method uses the principal component analysis theory to extract the maximum principal component of the effect of training on the ankle joint injury, and calculates the LASSO-Granger causality measurement values between the principal components. The kernel matrix of the principal component feature space is given and input in the support vector machine classifier which represents influence of training on ankle joint injury. The principal component characteristics of each influencing factor are marked, and the correlation between marked characteristics is obtained, and a new output kernel function is constructed. Based on this, the study on the impact of beach sports power training on ankle joint injury is completed. Simulation results showed that the accuracy of the proposed model is high, which can effectively ensure the smooth power training of beach sports and enhance the quality of beach sports power training.
The new science of bioball: Sensors and software protect baseball pitchers from injury

When St. Louis Cardinals pitcher Jordan Hicks [below] takes to the mound, batters tremble. He has the same form as any other pitcher in Major League Baseball (MLB) as he throws the ball, but his results are extraordinary. In the windup, he steps back on one leg, lifting his other leg to raise his center of gravity. Then he strides forward, his hips swiveling, while his throwing arm extends back nearly parallel to the ground. As his arm comes forward, the muscles and ligaments in his elbow absorb over 100 newton meters of torque.
Construction of Intelligent Learning Environment for Multiple Learning Methods

This paper introduces three principles of intelligent learning environment design and put forward to a construction model that pulls “facilities, platform systems and learning resources ”together, and strives to build a demonstration center for the deep integration of information technology and education. Then the paper takes three typical pedagogical applications as examples to show how intelligent learning space supporting innovative classroom, flipped classroom and remote interactive class.
Multimodal Ranked Search over Integrated Repository of Radiology Data Sources

Radiology teaching files serve as a reference in the diagnosis process and as a learning resource for radiology residents. Many public teaching file data sources are available online and private in-house repositories are maintained in most hospitals. However, the native interfaces for querying public repositories have limited capabilities. The Integrated Radiology Image Search (IRIS) Engine was designed to combine public data sources and in-house teaching files into a single resource. In this paper, we present and evaluate ranking strategies that prioritize the most relevant teaching files for a query. We quantify query context through a weighted text-based search and with ontology integration. We also incorporate an image-based search that allows finding visually similar teaching files. Finally, we augment text-based search results with image-based search – a hybrid approach that further improves search result relevance. We demonstrate that this novel approach to searching radiology data produces promising results by evaluating it with an expert panel of reviewers and by comparing our search performance against other publicly available search engines.
INNOVATIONS IN TRAINING SCIENTIFIC PERSONNEL OF HIGHER QUALIFICATION: INTERNATIONAL EXPERIENCE AND RUSSIAN REALITIES (EXPERT OPINION)

Modernization processes «from the top» of various social institutions in modern Russia have affected one of the most important – the Institute of education. The article deals with the aspect that has received a wide discourse in the domestic and foreign scientific community – training of highly qualified personnel in the Russian education system in the format of the international standard classification of education (ISCED). Training of Russian highly qualified personnel on the basis of postgraduate corresponds to the level of ISCED 8 – «doctorate or its equivalent» or ISCED level 8 – «Doctor or equivalent level», because in the European Union, according to the Bologna agreement (2003) there is no division into postgraduate and doctoral studies in the Russian sense. The article provides a comparative analysis and expert evaluation of scientific, organizational and pedagogical factors, procedures and criteria for the development and efficiency of the Russian Institute of postgraduate studies on the example of the functioning of the corresponding Institute of doctoral studies in the European Union, in Daugavpils University (Latvia). European experience in the organization and functioning of the Institute of doctoral studies shows that the Institute of postgraduate studies in Russia for four years is in the form of innovative, but the content of the destructive reform of reproduction of scientific and pedagogical personnel of higher qualification. According to the results of the study, conclusions are made about the directions of improving the training of highly qualified personnel in Russia, using some positive elements of the European experience in this field. In the theoretical aspect – the «unloading» of the educational programs of ISCED 8 due to the subjects previously studied in the master’s and specialist degrees. In practical aspect – to make wider use of the European promotion and scientific certification experience in the preparation of a scientific theses and the procedure for its protection.
Dynamic changes of QoS parameters in cloud computing service level agreement

Service level agreement (SLA) in cloud computing can be defined as a contractual agreement signed between cloud user and cloud provider in order to manage the quality of service (QoS) parameters which are the fundamental part of SLAs. The current dynamic SLA monitoring approaches have considered the cloud provider's perspective only. To take into consideration the user's perspective; this research proposes a dynamic SLA monitoring approach by generating new SLA terms based on analysing the SLA penalties. A new proposed trade-off mechanism is applied between monthly cost and QoS guarantee values. Thus; we propose a dynamic model for updating QoS parameters of SLA in cloud environment. Java language is used with a QoS measurement dataset to experiment the proposed approach. The current Static SLA (SSLA) approaches are compared with the proposed Dynamic SLA (DSLA) approach. The results of this experimental study show that the number of SLA violations in DSLA are less than the number of SLA violations in SSLA.
Ontology Learning from Twitter Data

Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved This paper presents and compares three mechanisms for learning an ontology describing a domain of discoursed as defined in a collection of tweets. The task in part involves the identification of entities and relations in the free text data, which can then be used to produce a set of RDF triples from which an ontology can be generated. The first mechanism is therefore founded on the Stanford CoreNLP Toolkit.; in particular the Named Entity Recognition and Relation Extraction mechanisms that come with this tool kit. The second is founded on the GATE General Architecture for Text Engineering which provides an alternative mechanism for relation extraction from text. Both require a substantial amount of training data. To reduce the training data requirement the third mechanism is founded on the concept of Regular Expressions extracted from a training data “seed set”. Although the third mechanism still requires training data the amount of training data is significantly reduced without adversely affecting the quality of the ontologies generated.
Fast Localization Algorithm of Eye Centers Based on Improved Hough Transform

Aiming at the problem of localization of eye centers in complex scenes, a method for quickly locating eye center is proposed in this paper. For the collected face images, this paper firstly uses bilateral filtering algorithm to remove the possible noise, and performs histogram equalization operation on the gray image to increase the dynamic range of the image grayscale and improve its distinguishability. Then, constructing cascaded strong classifier based on improved Ada Boost algorithm, and proposed three-layer eye detection. Finally, the method of canny operator edge detection and improved Hough circle detection is used to obtain the pupil center. The experimental results show that the algorithm can acquire the coordinates of the eye center quickly and accurately, and it is robust to eye location under illumination changes.
Coarse-to-fine kidney and tumor segmentation with fully convolutional networks

Segmentation is one of the most important tasks in medical image analysis.With the development of deep leaning, fully convolutional networks (FCNs) have become the dominant approach for this task and their extension to 3D achieved considerable improvements for automated organ segmentation in volumetric imaging data.In this paper we demonstrate a coarse-to-fine segmentation method using FCNs for Kits 2019 challenge.
Risikoorientierte Analyse und Handhabung von Anforderungsänderungen

Requirement changes are a major cause for project failure.A systematic approach to manage those changes from the very beginning should be an integral part of each development project.Although this is accepted in both science and industry, there is no adequate approach to tackle the issue, especially in the context of interdisciplinary systems.In this paper, a secondary analysis is done to identify all information that is necessary to manage those changes efficiently.The demanded information is pictured in a reference model and then mapped with the capabilities of existing approaches.Based on this, research gaps are identified and used to guide future research efforts.
Parallelization in reservoir computing based on a mutually coupled electro-optic delay system

We propose reservoir computing based on a mutually coupled electro-optic delay system. We experimentally show that the performance of reservoir computing can be improved using the mutually coupled system.
CLASSIFICATION BOOSTING IN IMBALANCED DATA

Most existing classification approaches assumed underlying training data set to be evenly distributed. However, in the imbalanced classification, the training data set of one majority class could far surpass those of the minority class. This becomes a problem because it’s usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over minority class. One popular method recently used to rectify this is the SMOTE (Synthetic Minority Over-Sampling Technique) which combines algorithms at data level. Therefore, this paper presents a novel approach for learning and imbalanced data sets, based on a combination of the SMOTE algorithm and the boosting procedure by focusing on a two-class problem. The Bidikmisi data set is imbalanced, because the distribution of majority class examples is 15 times the number of minority class examples. All models have been evaluated using stratified 5-fold cross-validation, and the performance criteria (such as Recall, F-Value and G-Mean) are examined. The results show that the SMOTE-Boosting algorithms have a better classification performance than the AdaBoost.M2 method, as the g-mean value increases 4-fold after the SMOTE method is used. We can say that SMOTE-Boosting algorithm is quite successful when taking advantage of boosting algorithms with SMOTE. When boosting affects the accuracy of the random forest by focusing on all data classes, the SMOTE algorithm alters the performance values of the random forest only in minority classes.
The Impact of The Development of Blended Learning Models Using Computer applications in Higher Education

This study showed that the impact of development of blended learning models can improve student learning outcomes, the learning process that utilizes information technology in the form of computers which equipped with telecommunications facilities (Internet, intranet, and multimedia (graphics, audio, video) as a medium in thedelivery of materials and interactions between teachers and learners. This method of research was R&amp;amp;D (Research and Development), which research was to produce products and test the effectiveness of the product. There were four phases of e-learning making, including (1) system analysis including hardware, software, learning and student development materials (2) designing, including instructional design analysis, teaching materials and media (software) (3) Implementation, namely the product and development of the resulting Learning Management System, (4) evaluation, which was the assessment stage by experts to test the feasibility of the System. This research was conducted at the Institute of Islamic Religion (IAIN) Manado Faculty of Economics and Islamic Business. Data collection techniques were conducted in three ways, namely (1) observation, (2) document analysis, and (3) interviews. The data validity technique in this study was the triangulation technique.
A TOI based CNN with Location Regression for Insurance Contract Analysis

Contract analysis with AI techniques can significantly ease the work for humans. This paper shows a problem of Element Tagging on Insurance Policy (ETIP). We present a novel Text-Of-Interest (TOI) convolutional neural network for the ETIP solution. We introduce a TOI pooling layer to replace traditional pooling layer for processing the nested phrasal or clausal elements in insurance policies. The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes. The computation of backpropagation through TOI pooling is also demonstrated in the paper. In addition, a location regressor is trained to improve the precision of element localization, called TOI-CNN+LR. In the detection, we devise a novel non-maximum suppression method with the fusion of length and score metrics, called LS-NMS. A large Chinese insurance contract dataset was collected to test the performance of the proposed method. An extensive set of experiments is performed to investigate how TOI-CNN+LR can work effectively in insurance elements tagging and outperforms other state-of-the-art nested NER models.
Towards Optimal Incentive-Driven Verification in Social Sensing Based Smart City Applications

Social sensing has emerged as a new smart city application paradigm that achieves low deployment costs by leveraging privately owned edge devices for data collection and processing. Unfortunately, such an application paradigm is vulnerable to result spoofing, where attackers fabricate computation results and convince the server that they are valid. This problem has not been adequately addressed due to three important challenges. First, the raw data originates at the edge, and therefore the application server cannot directly verify the computed results sent by the edge devices without receiving the raw data as well. Second, personally owned edge devices can easily collude to convince the server that fabricated results are real, even under sophisticated scrutiny. Third, the high level of churn common in such systems nullifies established reputation-based schemes. To address the above challenges we develop a novel incentive-driven verification system in which risk is minimized under a budget constraint. We compare our solution to state-of-the-art computation verification schemes through an extensive evaluation. The results show that our solution achieves the least risk of computation spoofing under a constrained budget when compared to all of the baselines.
A Head Pose Estimation Method Based on Multi-feature Fusion

Since head pose estimation is influenced by illumination variation, expression, noise disturbance and other factors, which results in low rate of recognition, a method of head pose estimation based on multi-feature fusion is proposed in this paper. At first, a pose feature combining the second-order histogram of oriented gradient (HOG) and the uniform pattern of local binary pattern (UP-LBP) is proposed, which is used for head pose estimation in single image. Then, an improved random forest algorithm is adopted for classification of head pose and solving the instability problem of the algorithm. Finally, the improved random forest classifier is used for head pose estimation of the novel pose feature. The experimental results show that, the method proposed in this paper is more capable of classification and with better robustness to illumination variation.